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If the rights waived or granted under applicable law in the relevant jurisdiction includes additional rights not waived or granted under this Document, these additional rights are included in this Document in order to meet the intent of this Document. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ad58f78c4d9237c045f76a9957e814c0cb06f462 --- /dev/null +++ b/README.md @@ -0,0 +1,75 @@ +--- +license: pddl +tags: +- eeg +- medical +- clinical +- classification +- mtbi +- tbi +- oddball +--- +# Cavanagh2019: EEG mTBI Classification Dataset with Auditory Oddball Task +The Cavanagh2019 dataset includes EEG recordings collected during a 3-stimulus auditory oddball paradigm in participants with mild traumatic brain injury (mTBI) and matched healthy controls. A total of 96 participants took part: 45 sub-acute mTBI patients (tested within 2 weeks post-injury), 26 healthy controls, and 25 chronic TBI patients (mild to moderate severity). Sub-acute mTBI and control participants completed two or three EEG sessions - at 3-14 days after the injury, and again after approximately 2 months - while chronic TBI participants completed a single session. + +The task involved 260 trials: 70% standard tones (440 Hz), 15% target tones (660 Hz), and 15% novel naturalistic sounds. Stimuli were presented binaurally, and participants were instructed to count target tones while ignoring the others. EEG was recorded from 60 channels at a 500 Hz sampling rate. +## Paper +Cavanagh, J. F., Wilson, J. K., Rieger, R. E., Gill, D., Broadway, J. M., Remer, J. H. S., Fratzke, V., Mayer, A. R., & Quinn, D. K. (2019). **ERPs predict symptomatic distress and recovery in sub-acute mild traumatic brain injury**. _Neuropsychologia_, 132, 107125. + +DISCLAIMER: We (DISCO) are NOT the owners or creators of this dataset, but we merely uploaded it here, to support our's ("EEG-Bench") and other's work on EEG benchmarking. +## Dataset Structure +- `data/` contains the annotated experiment EEG data. +- `scripts/` contains MATLAB scripts that produced the paper's results. +- `scripts/BigAgg_Data.mat` contains information about the subjects. +- `scripts/QUALITY_CHECK.xlsx` and `scripts/QUINN_QUALITY_CHECK.xlsx` contain information about bad quality recordings. + +A `.mat` file can be read in python as follows: +```python +from scipy.io import loadmat +mat = loadmat(filepath, simplify_cells=True) +``` +(A field "fieldname" can be read from `mat` as `mat["fieldname"]`.) + +Subject information can be read from `scripts/BigAgg_Data.mat` from the following fields (among others): +- `DEMO`: information about mTBI and control subjects + - `ID`: subject IDs, as included in the filename of the corresponding EEG recording under `data/` + - `Group_CTL1`: for each subject, whether it belongs to the control group (which is the case if and only if the corresponding `Group_CTL1`-entry is `1`) or not + - `Sex_F1`: gender of the subject (`1` means female, everything else means male) + - `Age`: age of the subject +- `Q_DEMO`: information about chronic TBI subjects + - `URSI`: subject IDs, as included in the filename of the corresponding EEG recording under `data/` + - `Sex_F1`: gender of the subject (`1` means female, everything else means male) + - `Age`: age of the subject +- `NP`: mTBI and control subjects' TOMM, TOPF, HVLT and other scores +- `Q_NP`: chronic TBI subjects' TOMM, TOPF, HVLT and other scores +- `QUEX`: mTBI and control BDI and other scores +- `Q_QUEX`: chronic TBI BDI and other scores +- `TBIfields`: information about mTBI subjects' injury +- `Q_TBIfields`: information about chronic TBI subjects' injury + +### Filename Format + +``` +[PID]_[SESSION]_3AOB.mat (or [PID]_[SESSION]_QUINN_3AOB.mat for chronic TBI participants) +``` +PID is the patient ID (e.g. `3001`), while SESSION distinguishes different days of recording (can be `1`, `2` or `3` for patients with mTBI or control patients and is always `1` for patients with chronic TBI). + +### Fields in each File +Let `mat` be an EEG `.mat` file from the `data/` directory. +Then `mat` contains (among others) the following fields and subfields +- `EEG` + - `data`: EEG data of shape `(#channels, trial_len, #trials)`. E.g. a recording of 247 trials/epochs with 60 channels, each trial having a duration of 4 seconds and a sampling rate of 500 Hz will have shape `(60, 2000, 247)`. + - `event`: Contains a list of dictionaries, each entry (each event) having the following description: + - `latency`: The onset of the event, measured as the index in the merged time-dimension `#trials x trial_len` (note `#trials` being the _outer_ and `trial_len` being the _inner_ array when merging). The duration of each event is 200ms. Hence, with a 500 Hz sampling rate, the EEG data `event_data` corresponding to the `i`-th event is + ```python + start_index = mat["EEG"]["event"][i]["latency"] + event_data = numpy.transpose(mat["EEG"]["data"], [1, 2]).reshape([num_channels, num_trials * trial_len])[:, start_index:start_index+100] # shape (#channels, 100) + ``` + - `type`: The type of event. Can be `"S200"` (660 Hz tone), `"S201"` (440 Hz tone) or `"S202"` (naturalistic). + - `chanlocs`: A list of channel descriptors + - `nbchan`: Number of channels + - `trials`: Number of trials/epochs in this recording + - `srate`: Sampling Rate (Hz) + +## License +By the original authors of this work, this work has been licensed under the PDDL v1.0 license (see LICENSE.txt). diff --git a/data/14000_1_QUINN_3AOB.mat b/data/14000_1_QUINN_3AOB.mat new file mode 100644 index 0000000000000000000000000000000000000000..d9b4423879c3a2a1de5a5188a5d7b2c0605a334b --- /dev/null +++ b/data/14000_1_QUINN_3AOB.mat @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:237765c1f2338d31a2542c2a10e558d1a1238fd70804d2ed94be218c1343c0f1 +size 477373827 diff --git a/data/18367_1_QUINN_3AOB.mat b/data/18367_1_QUINN_3AOB.mat new file mode 100644 index 0000000000000000000000000000000000000000..66153a6727e06a296ce39d7aca1d5e8773745e04 --- /dev/null +++ b/data/18367_1_QUINN_3AOB.mat @@ -0,0 +1,3 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mode 100644 index 0000000000000000000000000000000000000000..2be5f5efa3767e97a1f76cd53d70c928f6f774cf Binary files /dev/null and b/scripts/BigAgg_Data.mat differ diff --git a/scripts/Manuscript_3AOB.m b/scripts/Manuscript_3AOB.m new file mode 100644 index 0000000000000000000000000000000000000000..baad36d5ede97e728c518d0926c2da234b10ce3a --- /dev/null +++ b/scripts/Manuscript_3AOB.m @@ -0,0 +1,110 @@ +%% Step 3 3AOB +clear all; clc +addpath('Z:\EXPERIMENTS\mTBICoBRE\EEG\'); +datadir='Z:\EXPERIMENTS\mTBICoBRE\EEG\3AOB Processed\'; +homedir='Z:\EXPERIMENTS\mTBICoBRE\MANUSCRIPT 3AOB\'; +cd(homedir); + +load('Z:\EXPERIMENTS\mTBICoBRE\EEG\BV_Chanlocs_60.mat'); +tx2disp=-500:2:1000; + +% Load Data +s1_Load_Data; + +% Kill Data +s2_Kill_Data; + +%% Demographics + +s3_Demographics; + +sx_Predict_Attrition; + + +%% Example ERPs + +StdSite=find(strcmpi('FCz',{BV_Chanlocs_60.labels})); +StdT1=300; StdT2=450; + +TargSite=find(strcmpi('Pz',{BV_Chanlocs_60.labels})); +TargT1=400; TargT2=600; + +NovSite=find(strcmpi('FCz',{BV_Chanlocs_60.labels})); +NovT1=300; NovT2=450; + +ERPSITE=[StdSite,TargSite,NovSite]; +ERPWINS=[StdT1,StdT2;TargT1,TargT2;NovT1,NovT2]; +ERPWINS_tx2disp=[[find(tx2disp==StdT1),find(tx2disp==StdT2)];... + [find(tx2disp==TargT1),find(tx2disp==TargT2)];... + [find(tx2disp==NovT1),find(tx2disp==NovT2)] ]; + + +s4_Example_ERPs + +%% ERPs by Group + +time=1; + +s5_ERPs_by_Group + + +%% For SPSS + +s6_FOR_SPSS + +figure; boxplot(FORSPSS(:,[10,11,16,17,22,23])); % Raw, Scaled +skewness(FORSPSS(:,[10,11,16,17,22,23])) % not skewed + +% Calculate reliability for controls +CTL_REL=FORSPSS(FORSPSS(:,3)==1,:); + +[REL.rho.F12,REL.p.F12]=corr(CTL_REL(:,11),CTL_REL(:,17),'type','Spearman','rows','pairwise'); % F_Tot 1 & 2 +[REL.rho.F13,REL.p.F13]=corr(CTL_REL(:,11),CTL_REL(:,23),'type','Spearman','rows','pairwise'); % F_Tot 1 & 3 +[REL.rho.F23,REL.p.F23]=corr(CTL_REL(:,17),CTL_REL(:,23),'type','Spearman','rows','pairwise'); % F_Tot 2 & 3 + +[REL.rho.P3b12,REL.p.P3b12]=corr(CTL_REL(:,11+1),CTL_REL(:,17+1),'type','Spearman','rows','pairwise'); % P3b 1 & 2 +[REL.rho.P3b13,REL.p.P3b13]=corr(CTL_REL(:,11+1),CTL_REL(:,23+1),'type','Spearman','rows','pairwise'); % P3b 1 & 3 +[REL.rho.P3b23,REL.p.P3b23]=corr(CTL_REL(:,17+1),CTL_REL(:,23+1),'type','Spearman','rows','pairwise'); % P3b 2 & 3 + +[REL.rho.P3a12,REL.p.P3a12]=corr(CTL_REL(:,11+2),CTL_REL(:,17+2),'type','Spearman','rows','pairwise'); % P3a 1 & 2 +[REL.rho.P3a13,REL.p.P3a13]=corr(CTL_REL(:,11+2),CTL_REL(:,23+2),'type','Spearman','rows','pairwise'); % P3a 1 & 3 +[REL.rho.P3a23,REL.p.P3a23]=corr(CTL_REL(:,17+2),CTL_REL(:,23+2),'type','Spearman','rows','pairwise'); % P3a 2 & 3 + +%% Correlations + +DV=IDENTITY.QUEX(:,find(strcmp('F_Tot',IDENTITY_QUEX_HDR))); + +time=1; +CONDI4Corr=3; % Std, Targ, Nov + +s6_Correlations + +%% Predictions + +quexidx=find(strcmp('F_Tot',IDENTITY_QUEX_HDR)); +CONDI4Corr=2; % Std, Targ, Nov + +s6_Correlations_S1EEG_With_FrSBediffs + +%% -------------- Between-Group rho-to-z + +% Just type 'em in here from the plots (remember number on plots is df, not N): + +r1=-.11 +n1=38 +r2=-.46 +n2=23 + +clc; + +t_r1 = 0.5*log((1+r1)/(1-r1)); +t_r2 = 0.5*log((1+r2)/(1-r2)); +z = (t_r1-t_r2)/sqrt(1/(n1-3)+1/(n2-3)) +p = (1-normcdf(abs(z),0,1))*2 + + +%% -------------- Within-Group rho-to-z + +s7_Mengs_z + + diff --git a/scripts/ORIGINAL_README.txt b/scripts/ORIGINAL_README.txt new file mode 100644 index 0000000000000000000000000000000000000000..312c0f2160884cc9b1e22807ebd34913c42be7de --- /dev/null +++ b/scripts/ORIGINAL_README.txt @@ -0,0 +1,10 @@ +All data are the output of STEP1_3AOB_JFC.m. I didn't include raw data here since that contains things like date and time that need to be stripped out. + +Files are labeled: ID _ session _ task.mat (e.g. 3001_1_3AOB.mat) + +These have already been cleaned using APPLE; bad ICs are identified in the QUALITY CHECK.xls sheets. + +I could be convinced to convert the raw data to .mat data so it could be run through STEP1 and people could clean it as they see fit - but otherwise having the data already pre-processed may help people get to some results quicker. + +You can run STEP2_3AOB_Process.m to get the processed data, then run Manuscript_3AOB.m to call all the sub-routines that output the data in the paper. + diff --git a/scripts/QUALITY_CHECK.xlsx b/scripts/QUALITY_CHECK.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..cf71af9f761ed3f0a7debfcacec78987a2d450ab Binary files /dev/null and b/scripts/QUALITY_CHECK.xlsx differ diff --git a/scripts/QUINN_QUALITY_CHECK.xlsx b/scripts/QUINN_QUALITY_CHECK.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..68b290b37c4cb277988daa72b252c4101637e055 Binary files /dev/null and b/scripts/QUINN_QUALITY_CHECK.xlsx differ diff --git a/scripts/Run_Thresh_1D.m b/scripts/Run_Thresh_1D.m new file mode 100644 index 0000000000000000000000000000000000000000..69a6133e8936fee9a6d147fe92a19fdf2373b16f --- /dev/null +++ b/scripts/Run_Thresh_1D.m @@ -0,0 +1,61 @@ +function [Corrected_P] = Run_Thresh_1D(TEMP1,TEMP2,site,ttesttype) + +SHUFFLES=5000; +for shuffi=1:SHUFFLES + + tempA=squeeze(mean(TEMP1(:,site,251:1001),2)); + tempB=squeeze(mean(TEMP2(:,site,251:1001),2)); + tempAB=[tempA;tempB]; + idx=shuffle([ones(1,25),zeros(1,25)]); + A=tempAB(idx==1,:); + B=tempAB(idx==0,:); + + if strmatch(ttesttype,'between') + [H,P,CI,STATS]=ttest2(A,B); + elseif strmatch(ttesttype,'within') + [H,P,CI,STATS]=ttest(A,B); + end + P(P<=.05)=NaN; P(P>.05)=0; P(isnan(P))=1; + + P=squeeze(P); + l=bwlabel(P); + if max(l)>0 + for ei=1:max(l) + idxs = find(l == ei); + tempthresh(ei) = sum(abs(STATS.tstat(idxs))); + end + THRESH(shuffi) = max(tempthresh); + clear idxs tempthresh ; + else + THRESH(shuffi) = 0; + end + clear H CI P STATS temp* A B idx l dims lmax; + +end +THRESH=sort(THRESH); +ThisThreshold=THRESH(end-SHUFFLES*.05); + + +% NOW Run 1D size of effects +if strmatch(ttesttype,'between') + [H,P,CI,STATS]=ttest2(squeeze(mean(TEMP1(:,site,251:1001),2)),squeeze(mean(TEMP2(:,site,251:1001),2))); +elseif strmatch(ttesttype,'within') + [H,P,CI,STATS]=ttest(squeeze(mean(TEMP1(:,site,251:1001),2)),squeeze(mean(TEMP2(:,site,251:1001),2))); +end +P(P<=.05)=NaN; P(P>.05)=0; P(isnan(P))=1; + +P=squeeze(P); +l=bwlabel(P); +Corrected_P=NaN*ones(1,751); +if max(l)>0 + for ei=1:max(l) + idxs = find(l == ei); + if sum(abs(STATS.tstat(idxs))) > ThisThreshold + Corrected_P(idxs) = 1; + end + end +end +clear H CI P STATS temp* A B idx l dims lmax idxs; + +clear THRESH ThisThreshold + diff --git a/scripts/STEP1_3AOB_JFC.m b/scripts/STEP1_3AOB_JFC.m new file mode 100644 index 0000000000000000000000000000000000000000..e04b3e0f3da3cf5758e0c2321c6b0635c0be95f3 --- /dev/null +++ b/scripts/STEP1_3AOB_JFC.m @@ -0,0 +1,192 @@ +%% 3AOB JFC +clear all; clc +addpath('Z:\EXPERIMENTS\mTBICoBRE\EEG\'); +addpath(genpath('Y:\Programs\eeglab12_0_2_1b')); + rmpath('Y:\Programs\eeglab12_0_2_1b\functions\octavefunc'); +rmpath('Y:\Programs\eeglab14_0_0b\functions\octavefunc'); +datadir='Y:\EEG_Data\mTBICoBRE\'; % Data are here +saveloc='Z:\EXPERIMENTS\mTBICoBRE\EEG\3AOB Preproc\'; +load('Z:\EXPERIMENTS\mTBICoBRE\EEG\BV_Chanlocs_60.mat'); +cd(saveloc); + +sx_dirs=dir([datadir,'M*']); +for sxi=1:length(sx_dirs) + for ses=1:3 + sessdir=[datadir,sx_dirs(sxi).name,'\eeg\RawEEG\']; + sx_sess{sxi}{1,ses}=dir([sessdir,'*_',num2str(ses),'_ODDBALL.vhdr']); + sx_sess{sxi}{2,ses}=sessdir; + sx_sess{sxi}{3,ses}=sx_dirs(sxi).name; + end +end +LOG=[]; +for sxi=1:size(sx_sess,2) + for sess=1:3 + if ~isempty( sx_sess{sxi}{1,sess} ) + subno=str2num(sx_sess{sxi}{1,sess}.name(1:4)); + URSI=sx_sess{sxi}{3,sess}; + LOG(subno-3000,sess+1)=subno; + LOG(subno-3000,1)=str2num(URSI(end-4:end)); + end + end +end + +for sxi=1:size(sx_sess,2) + for sess=1:3 + if ~isempty( sx_sess{sxi}{1,sess} ) + + subno=str2num(sx_sess{sxi}{1,sess}.name(1:4)); + thisdir=sx_sess{sxi}{2,sess}; + URSI=sx_sess{sxi}{3,sess}; + LOG2(sxi,sess)=subno; + + if ~exist([saveloc,num2str(subno),'_',num2str(sess),'_3AOB.mat']); + + % Data are 65 chans: 1=63 is EEG, 64 is VEOG, 65 is EKG Ref'd to CPz - - will want to retrieve that during re-referencing + EEG = pop_loadbv(thisdir,[num2str(subno),'_',num2str(sess),'_ODDBALL.vhdr']); clc; disp(['Loading ',num2str(subno),' s',num2str(sess)]); + % Run PATCH for sx<3003 s<2 AND for bad templates + PATCH + % Get Locs + locpath=('Y:\Programs\eeglab12_0_2_1b\plugins\dipfit2.2\standard_BESA\standard-10-5-cap385.elp'); + EEG = pop_chanedit(EEG, 'lookup', locpath); + EEG = eeg_checkset( EEG ); + % Get event types + for ai=2:length(EEG.event); clear temp; temp=EEG.event(ai).type; + if isempty(strmatch('boundary',temp)); TYPES(ai)=str2num(temp(2:end)) ; clear temp; end + end + UNIQUE_TYPES=unique(TYPES); + for ai=1:length(UNIQUE_TYPES); UNIQUE_TYPES_COUNT(ai)=sum(TYPES==UNIQUE_TYPES(ai)); end + clc; TRIGGERS=[UNIQUE_TYPES;UNIQUE_TYPES_COUNT] % Trigger type, Frequency + + % Epoch + All_STIM={'S201','S200','S202'}; % Std, Target, Novel + EEG = pop_epoch( EEG, All_STIM, [-2 2], 'newname', 'Epochs', 'epochinfo', 'yes'); + EEG = eeg_checkset( EEG ); + % Remove VEOG and EKG + EEG.EKG=squeeze(EEG.data(65,:,:)); + EEG.VEOG=squeeze(EEG.data(64,:,:)); + EEG.data=EEG.data(1:63,:,:); + EEG.nbchan=63; + EEG.chanlocs(65)=[]; EEG.chanlocs(64)=[]; + % Fix BV-specific issue - - - only needed for APPLE + for ai=1:size(EEG.urevent,2), EEG.urevent(ai).bvtime=EEG.urevent(ai).bvmknum; end + for ai=1:size(EEG.event,2), EEG.event(ai).bvtime=EEG.event(ai).bvmknum; end + for ai=1:size(EEG.epoch,2), EEG.epoch(ai).eventbvtime=EEG.epoch(ai).eventbvmknum; end + % Add CPz + EEG = pop_chanedit(EEG,'append',63,'changefield',{64 'labels' 'CPz'}); + EEG = pop_chanedit(EEG,'lookup', locpath); + % Re-Ref to Average Ref and recover CPz + EEG = pop_reref(EEG,[],'refloc',struct('labels',{'CPz'},'type',{''},'theta',{180},'radius',{0.12662},'X',{-32.9279},'Y',{-4.0325e-15},'Z',{78.363},... + 'sph_theta',{-180},'sph_phi',{67.208},'sph_radius',{85},'urchan',{64},'ref',{''}),'keepref','on'); + % Remove everything else NOW that CPz has been reconstructed from the total + EEG.MASTOIDS = squeeze(mean(EEG.data([10,21],:,:),1)); + EEG.data = EEG.data([1:4,6:9,11:20,22:26,28:64],:,:); + EEG.nbchan=60; + EEG.chanlocs(27)=[]; EEG.chanlocs(21)=[]; EEG.chanlocs(10)=[]; EEG.chanlocs(5)=[]; % Have to be in this order! + % Should probably re-ref to average again now that the contaminated channels are gone + EEG = pop_reref(EEG,[]); + % Remove mean + EEG = pop_rmbase(EEG,[],[]); + + + % ---------------------- + % Setup APPLE to interp chans, reject epochs, & ID bad ICs. Output will be Avg ref'd and ICA'd. + eeg_chans=1:60; + Do_ICA=1; + ref_chan=36; % Re-Ref to FCz [WEIRD STEP, BUT THIS IS FOR FASTER, which is a part of APPLE] + EEG = pop_reref(EEG,ref_chan,'keepref','on'); + + % Run APPLE (will re-ref data to avg ref) + [EEG,EEG.bad_chans,EEG.bad_epochs,EEG.bad_ICAs]=APPLE_3AOB(EEG,eeg_chans,ref_chan,Do_ICA,subno,EEG.VEOG,sess,BV_Chanlocs_60); + + % Save + save([num2str(subno),'_',num2str(sess),'_3AOB.mat'],'EEG'); + % ---------------------- + + %% Remove the (presumptive) bad ICAs: + bad_ICAs_To_Remove=EEG.bad_ICAs{2}; + if bad_ICAs_To_Remove==0, bad_ICAs_To_Remove=1; end + EEG = pop_subcomp( EEG, bad_ICAs_To_Remove, 0); + + + % Get the good info out of the epochs + for ai=1:size(EEG.epoch,2) + % Initialize + EEG.epoch(ai).CUE=NaN; + for bi=1:size(EEG.epoch(ai).eventlatency,2) + % Get STIMTYPE + if EEG.epoch(ai).eventlatency{bi}==0 && isempty(strmatch(EEG.epoch(ai).eventtype{bi},'N999')); % If this bi is the event + % Get StimType + FullName=EEG.epoch(ai).eventtype{bi}; + EEG.epoch(ai).CUE=str2num(FullName(2:end)) ; + clear FullName + VECTOR(ai,1)=EEG.epoch(ai).CUE; + end + end + end + + % Let's just do this for display + dims=size(EEG.data); + EEG.data=eegfilt(EEG.data,500,[],20); + EEG.data=reshape(EEG.data,dims(1),dims(2),dims(3)); + + % Set Params + tx=-2000:2:1998; + b1=find(tx==-200); b2=find(tx==0); + t1=find(tx==-500); t2=find(tx==1000); + toporange1=find(tx==250); toporange2=find(tx==600); toporangetot=250:2:600; + tx2disp=-500:2:1000; + MAPLIMS=[-8 8]; + + % Basecor your ERPs here so they are pretty. + BASE=squeeze( mean(EEG.data(:,b1:b2,:),2) ); + for ai=1:dims(1) + EEG.data(ai,:,:)=squeeze(EEG.data(ai,:,:))-repmat( BASE(ai,:),dims(2),1 ); + end + + + % Get max of P2 across all condis + site=11; % Pz + ERP4topo=mean(EEG.data(site,toporange1:toporange2,VECTOR(:,1)==200),3); + topomax_P3b=toporangetot(find(ERP4topo==max(ERP4topo))); + topotoplot_P3b=find(tx==topomax_P3b); + site=36; % FCz + ERP4topo=mean(EEG.data(site,toporange1:toporange2,VECTOR(:,1)==202),3); + topomax_P3a=toporangetot(find(ERP4topo==max(ERP4topo))); + topotoplot_P3a=find(tx==topomax_P3a); + % -------------- + figure; + site=11; % Pz + subplot(3,4,1:4); hold on + plot(tx2disp,mean(EEG.data(site,t1:t2,VECTOR(:,1)==201),3),'k'); + plot(tx2disp,mean(EEG.data(site,t1:t2,VECTOR(:,1)==200),3),'r'); + plot(tx2disp,mean(EEG.data(site,t1:t2,VECTOR(:,1)==202),3),'b'); + plot([topomax_P3b topomax_P3b],[-2 2],'m','linewidth',2); % indicate the max with a magenta line + title(['Pz Subno: ',num2str(subno),' Sess:',num2str(sess)]); + legend({'Std','Target','Novel'},'Location','NorthWest'); + % -------------- + site=36; % FCz + subplot(3,4,5:8); hold on + plot(tx2disp,mean(EEG.data(site,t1:t2,VECTOR(:,1)==201),3),'k'); + plot(tx2disp,mean(EEG.data(site,t1:t2,VECTOR(:,1)==200),3),'r'); + plot(tx2disp,mean(EEG.data(site,t1:t2,VECTOR(:,1)==202),3),'b'); + plot([topomax_P3a topomax_P3a],[-2 2],'m','linewidth',2); % indicate the max with a magenta line + title(['FCz Subno: ',num2str(subno),' Sess:',num2str(sess)]); + % -------------- + subplot(3,4,9); hold on + topoplot( mean(EEG.data(:,topotoplot_P3b,VECTOR(:,1)==201),3) , BV_Chanlocs_60,'maplimits',MAPLIMS); title('Std @ P3b') + subplot(3,4,10); hold on + topoplot( mean(EEG.data(:,topotoplot_P3b,VECTOR(:,1)==200),3) , BV_Chanlocs_60,'maplimits',MAPLIMS); title('Targ') + subplot(3,4,11); hold on + topoplot( mean(EEG.data(:,topotoplot_P3a,VECTOR(:,1)==202),3) , BV_Chanlocs_60,'maplimits',MAPLIMS); title('Novel') + + saveas(gcf, [num2str(subno),'_',num2str(sess),'_3AOB_ERPs.png'],'png'); + close all; + + clear EEG VECTOR BASE PROBE TRIGGERS TYPES UNIQUE* did* topo* ERP* URSI dims eeg_chans; + end + end + end +end + +%% + diff --git a/scripts/STEP2_3AOB_Process.m b/scripts/STEP2_3AOB_Process.m new file mode 100644 index 0000000000000000000000000000000000000000..e166126fcb4a4d2c0d0b5f8de8d715b06a8e281d --- /dev/null +++ b/scripts/STEP2_3AOB_Process.m @@ -0,0 +1,250 @@ +%% Step 2 Oddball + +clear all; clc +addpath('Z:\EXPERIMENTS\mTBICoBRE\EEG\'); +savedir='Z:\EXPERIMENTS\mTBICoBRE\EEG\3AOB Processed\'; + +load('Z:\EXPERIMENTS\mTBICoBRE\EEG\BV_Chanlocs_60.mat'); + +% ########## For Cavanagh data +datadir='Z:\EXPERIMENTS\mTBICoBRE\EEG\3AOB Preproc\'; +[D_DAT,D_HDR,D_ALL]=xlsread('Z:\EXPERIMENTS\mTBICoBRE\ANALYSIS\QUALITY_CHECK.xlsx','ODDBALL_ICAs'); +FILEENDER='_3AOB.mat'; + +% % % ########## For Quinn data +% % datadir='Z:\EXPERIMENTS\mTBICoBRE\EEG\QUINN 3AOB Preproc\'; +% % [D_DAT,D_HDR,D_ALL]=xlsread('Z:\EXPERIMENTS\mTBICoBRE\ANALYSIS\QUINN_QUALITY_CHECK.xlsx','ODDBALL_ICAs'); +% % FILEENDER='_QUINN_3AOB.mat'; + +cd(datadir); + +% ############# Set Params +srate=500; +tx=-2000:1000/srate:1998; +B1=find(tx==-300); B2=find(tx==-200); +T1=find(tx==-500); T2=find(tx==1000); +tx2disp=-500:2:1000; +% ############# + + +for si=1:length(D_DAT) + for sess=1:size(D_DAT,2)-1 % should be '2' for Quinn data, '3' for Cavanagh data + + subno=D_DAT(si,1); + skip=0; + + INFO=D_ALL{si+1,sess+1}; % +1's b/c of subno column and header row + disp(['TRYOUT ',num2str(subno),' S',num2str(sess)]); + + if isnumeric(INFO), bad_ICAs_To_Remove=INFO; end + if isnan(INFO), skip=1; end % not done yet + if strmatch('BAD',INFO), skip=1; end % Bad data + if ~isnumeric(INFO), bad_ICAs_To_Remove=str2num(INFO); end + + % Don't repeat if already done + if exist([savedir,num2str(subno),'_',num2str(sess),'_3AOB_TFandERPs_L.mat'])==2, skip=1; end + + if skip==0 + + load([num2str(subno),'_',num2str(sess),FILEENDER]); disp(['DOING: ',num2str(subno),'_',num2str(sess),'_3AOB.mat']); + + % Remove the bad ICAs: + disp(['BAD ICAS: ', num2str(bad_ICAs_To_Remove)]); + EEG = pop_subcomp( EEG, bad_ICAs_To_Remove, 0); + + % Get the good info out of the epochs + for ai=1:size(EEG.epoch,2) + % Initialize + EEG.epoch(ai).EEG=NaN; + for bi=1:size(EEG.epoch(ai).eventlatency,2) + % Get STIMTYPE + if EEG.epoch(ai).eventlatency{bi}==0 && isempty(strmatch(EEG.epoch(ai).eventtype{bi},'N999')); % If this bi is the event + % Get StimType + FullName=EEG.epoch(ai).eventtype{bi}; + EEG.epoch(ai).EEG=str2num(FullName(2:end)) ; + + clear FullName + VECTOR(ai,1)=EEG.epoch(ai).EEG; All_STIM={'S201','S200','S202'}; % Std, Target, Novel + end + end + end + + % Only as many STD as NOV + N_n=sum(VECTOR(:,1)==202); + temp_idxs=find(VECTOR(:,1)==201); + temp_idxs=shuffle(temp_idxs); + VECTOR(temp_idxs(N_n+1:end),1)=999; clear temp_idxs; + % Save trial counts + TRL_ct(1)=sum(VECTOR(:,1)==201); + TRL_ct(2)=sum(VECTOR(:,1)==200); + TRL_ct(3)=sum(VECTOR(:,1)==202); + + + %% + % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ + % $$$$$$$$$$$$$$$$$$$$$$$ Time-Freq + % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ + + % Setup Wavelet Params + num_freqs=50; + frex=logspace(.01,1.7,num_freqs); + s=logspace(log10(3),log10(10),num_freqs)./(2*pi*frex); + t=-2:1/EEG.srate:2; + + % Definte Convolution Parameters + dims = size(EEG.data); + n_wavelet = length(t); + n_data = dims(2)*dims(3); + n_convolution = n_wavelet+n_data-1; + n_conv_pow2 = pow2(nextpow2(n_convolution)); + half_of_wavelet_size = (n_wavelet-1)/2; + + % For Laplacian + X = [BV_Chanlocs_60.X]; Y = [BV_Chanlocs_60.Y]; Z = [BV_Chanlocs_60.Z]; + + % Pick channel + chans=[36,33,56]; % FCz, F5, F6 + + for REFi=1:2 + if REFi==1, TAG='V'; + elseif REFi==2, TAG='L'; + [EEG.data,~,~] = laplacian_perrinX(EEG.data,X,Y,Z,[],1e-6); + end + + % Get FFT of data + for chani=1:3 + EEG_fft(chani,:) = fft(reshape(EEG.data(chans(chani),:,:),1,n_data),n_conv_pow2); + end + + for fi=1:num_freqs + + wavelet = fft( exp(2*1i*pi*frex(fi).*t) .* exp(-t.^2./(2*(s(fi)^2))) , n_conv_pow2 ); % sqrt(1/(s(fi)*sqrt(pi))) * + + % convolution + for chani=1:3 + temp_conv = ifft(wavelet.*EEG_fft(chani,:)); + temp_conv = temp_conv(1:n_convolution); + temp_conv = temp_conv(half_of_wavelet_size+1:end-half_of_wavelet_size); + EEG_conv(chani,:,:) = reshape(temp_conv,dims(2),dims(3)); + clear temp_conv; + + % Common pre-EEG baseline + temp_BASE(chani,:) = mean(mean(abs(EEG_conv(chani,B1:B2,:)).^2,2),3); + end + + for idx=1:3 + + if idx==1, idx_V=VECTOR(:,1)==201; % STD + elseif idx==2, idx_V=VECTOR(:,1)==200; % TARG + elseif idx==3, idx_V=VECTOR(:,1)==202; % NOV + end + + for chani=1:3 + temp_PWR = squeeze(mean(abs(EEG_conv(chani,T1:T2,idx_V)).^2,3)); + + POWER(chani,fi,:,idx) = 10* ( log10(temp_PWR') - log10(repmat(temp_BASE(chani,:),size(tx2disp,2),1)) ); + ITPC(chani,fi,:,idx) = abs(mean(exp(1i*( angle(EEG_conv(chani,T1:T2,idx_V)) )),3)); + if chani==1, seed=1; targ=2; + elseif chani==2, seed=1; targ=3; + elseif chani==3, seed=2; targ=3; + end + ISPC(chani,fi,:,idx) = abs(mean(exp(1i*( angle(EEG_conv(seed,T1:T2,idx_V)) - angle(EEG_conv(targ,T1:T2,idx_V)) )),3)); + + clear temp_PWR; + end + clear idx_V ; + end + clear wavelet idx_V temp_BASE EEG_conv; + end + + %% + % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ + % $$$$$$$$$$$$$$$$$$$$$$$ Theta Topo + % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ + + topofrex=4.5; + s=logspace(log10(3),log10(10),num_freqs)./(2*pi*topofrex); + wavelet = fft( exp(2*1i*pi*frex(fi).*t) .* exp(-t.^2./(2*(s(fi)^2))) , n_conv_pow2 ); % sqrt(1/(s(fi)*sqrt(pi))) * + + seed=36; + + EEG_fft_4TOPO = fft(reshape(EEG.data(seed,:,:),1,n_data),n_conv_pow2); + seed_EEG_conv_4TOPO = ifft(wavelet.*EEG_fft_4TOPO); + seed_EEG_conv_4TOPO = seed_EEG_conv_4TOPO(1:n_convolution); + seed_EEG_conv_4TOPO = seed_EEG_conv_4TOPO(half_of_wavelet_size+1:end-half_of_wavelet_size); + seed_EEG_conv_4TOPO = reshape(seed_EEG_conv_4TOPO,dims(2),dims(3)); + clear EEG_fft_4TOPO ; + + % Common pre-EEG SEED baseline + seed_BASE = mean(mean(abs(seed_EEG_conv_4TOPO(B1:B2,:)).^2,1),2); + + for chani=1:60 + + EEG_fft_4TOPO = fft(reshape(EEG.data(chani,:,:),1,n_data),n_conv_pow2); + EEG_conv_4TOPO = ifft(wavelet.*EEG_fft_4TOPO); + EEG_conv_4TOPO = EEG_conv_4TOPO(1:n_convolution); + EEG_conv_4TOPO = EEG_conv_4TOPO(half_of_wavelet_size+1:end-half_of_wavelet_size); + EEG_conv_4TOPO = reshape(EEG_conv_4TOPO,dims(2),dims(3)); + + % Common pre-EEG baseline + temp_BASE = mean(mean(abs(EEG_conv_4TOPO(B1:B2,:)).^2,1),2); + + for idx=1:3 + if idx==1, idx_V=VECTOR(:,1)==201; % STD + elseif idx==2, idx_V=VECTOR(:,1)==200; % TARG + elseif idx==3, idx_V=VECTOR(:,1)==202; % NOV + end + + temp_PWR = squeeze(mean(abs(EEG_conv_4TOPO(T1:T2,idx_V)).^2,2)); + POWER_TOPO(chani,:,idx) = 10* ( log10(temp_PWR) - log10(repmat(temp_BASE,size(tx2disp,2),1)) ); + + S4cor=10* ( log10(abs(seed_EEG_conv_4TOPO(T1:T2,idx_V)).^2) - log10(repmat(seed_BASE,size(tx2disp,2),sum(idx_V))) ); + T4cor=10* ( log10(abs(EEG_conv_4TOPO(T1:T2,idx_V)).^2) - log10(repmat(temp_BASE,size(tx2disp,2),sum(idx_V))) ); + CORREL_TOPO(chani,:,idx)= diag(corr(S4cor',T4cor','type','Spearman')); + + SYNCH_TOPO(chani,:,idx) = abs(mean(exp(1i*( angle(seed_EEG_conv_4TOPO(T1:T2,idx_V)) - angle(EEG_conv_4TOPO(T1:T2,idx_V)) )),2)); + + clear idx_V temp_PWR S4cor T4cor; + end + + clear EEG_fft_4TOPO EEG_conv_4TOPO TOPO_conv temp_BASE; + end + + %% + % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ + % $$$$$$$$$$$$$$$$$$$$$$$ ERPs + % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ + + % Filter + dims=size(EEG.data); + EEG.data=eegfilt(EEG.data,500,[],20); + EEG.data=eegfiltfft(EEG.data,500,.1,[]); + EEG.data=reshape(EEG.data,dims(1),dims(2),dims(3)); + + % Basecor your ERPs here so they are pretty. + EEG_BASE=squeeze( mean(EEG.data(:,find(tx==-200):find(tx==0),:),2) ); + for ai=1:dims(1) + EEG.data(ai,:,:)=squeeze(EEG.data(ai,:,:))-repmat( EEG_BASE(ai,:),dims(2),1 ); + end + + % Get ERPs + for idx=1:3 + if idx==1, idx_V=VECTOR(:,1)==201; % STD + elseif idx==2, idx_V=VECTOR(:,1)==200; % TARG + elseif idx==3, idx_V=VECTOR(:,1)==202; % NOV + end + ERP(:,:,idx)=squeeze(mean(EEG.data(:,find(tx==-500):find(tx==1000),idx_V),3)); + clear DATA_erp idx_V ; + end + + save([savedir,num2str(subno),'_',num2str(sess),'_3AOB_TFandERPs_',TAG,'.mat'],'ERP','ISPC','ITPC','POWER','VECTOR','SYNCH_TOPO','TRL_ct','POWER_TOPO','CORREL_TOPO'); + + clear ERP ISPC ITPC POWER RT; + end + + clearvars -except datadir savedir FILEENDER BV_Chanlocs_60 D_DAT D_HDR D_ALL tx B1 B2 T1 T2 tx2disp si sess + end + end +end + +%% diff --git a/scripts/s1_Load_Data.m b/scripts/s1_Load_Data.m new file mode 100644 index 0000000000000000000000000000000000000000..9e241d850f78b4f2f82a557134e7a8e7d1d5fd7d --- /dev/null +++ b/scripts/s1_Load_Data.m @@ -0,0 +1,121 @@ + +cd(datadir); + +filz=dir(['*_3AOB_TFandERPs_V.mat']); +Nsubjs=length(filz); + +% Load BigAgg_Data +load('Z:\EXPERIMENTS\mTBICoBRE\MANUSCRIPT 3AOB\BigAgg_Data.mat'); + +% Preallocate +IDENTITY.DEMO=NaN(Nsubjs,7); +IDENTITY.TBI=NaN(Nsubjs,9); +IDENTITY.NP=NaN(Nsubjs,8); +IDENTITY.QUEX=NaN(Nsubjs,25); +% ^^^^^^^^^^ +MEGA_PWR=NaN(Nsubjs,3,50,751,3); +MEGA_PHS=NaN(Nsubjs,3,50,751,3); +MEGA_SYNCH=NaN(Nsubjs,3,50,751,3); +MEGA_SYNCH_TOPO=NaN(Nsubjs,60,751,3); +MEGA_POWER_TOPO=NaN(Nsubjs,60,751,3); +MEGA_CORREL_TOPO=NaN(Nsubjs,60,751,3); +MEGA_ERP=NaN(Nsubjs,60,751,3); +MEGA_TRL_ct=NaN(Nsubjs,3); + +for si=1:Nsubjs + + subno = str2double(filz(si).name(1:end-23)) ; % B/C some Quinn ones have 5 digit IDs, some 4 + session = str2double(filz(si).name(end-21)) ; + + if subno<3500 % Cavanagh + if mod(subno,2)==1, group=1; % ODD - Ctl + elseif mod(subno,2)==0,group=2; % EVEN - mTBI + end + else % Quinn + group=3; % Chronic TBI (cTBI) + end + + IDENTITY_DEMO_HDR{1}={'subno'}; IDENTITY_DEMO_HDR{2}='session'; IDENTITY_DEMO_HDR{3}='group'; + IDENTITY_TBI_HDR{1}={'subno'}; IDENTITY_TBI_HDR{2}='session'; IDENTITY_TBI_HDR{3}='group'; + IDENTITY_NP_HDR{1}={'subno'}; IDENTITY_NP_HDR{2}='session'; IDENTITY_NP_HDR{3}='group'; + IDENTITY_QUEX_HDR{1}={'subno'}; IDENTITY_QUEX_HDR{2}='session'; IDENTITY_QUEX_HDR{3}='group'; + + IDENTITY.DEMO(si,1:3)=[subno,session,group]; + IDENTITY.TBI(si,1:3)=[subno,session,group]; + IDENTITY.NP(si,1:3)=[subno,session,group]; + IDENTITY.QUEX(si,1:3)=[subno,session,group]; + + % --------------- QUEX + if group<3 % Cavanagh data + if any(DEMO.ID(:,1)==subno) + bigagg_idx=find(DEMO.ID(:,1)==subno); + IDENTITY.DEMO(si,4)=DEMO.URSI(bigagg_idx,1); IDENTITY_DEMO_HDR{4}='URSI'; + IDENTITY.DEMO(si,5)=DEMO.Sex_F1(bigagg_idx); IDENTITY_DEMO_HDR{5}='SexF1'; + IDENTITY.DEMO(si,6)=DEMO.Age(bigagg_idx); IDENTITY_DEMO_HDR{6}='Age'; + IDENTITY.DEMO(si,7)=DEMO.SES(bigagg_idx); IDENTITY_DEMO_HDR{7}='YrsEd'; % That's what this actually is. + if session==1 + IDENTITY.TBI(si,4)=TBIfields.Glasgow(bigagg_idx); IDENTITY_TBI_HDR{4}='GCS'; + IDENTITY.TBI(si,5)=TBIfields.LOC(bigagg_idx); IDENTITY_TBI_HDR{5}='LOC'; + IDENTITY.TBI(si,6)=TBIfields.LOCtime(bigagg_idx); IDENTITY_TBI_HDR{6}='LOCtime'; + IDENTITY.TBI(si,7)=TBIfields.LOM(bigagg_idx); IDENTITY_TBI_HDR{7}='LOM'; + IDENTITY.TBI(si,8)=TBIfields.DaysSinceInjury(bigagg_idx); IDENTITY_TBI_HDR{8}='Days'; + % --------------------- + IDENTITY.NP(si,4)=NP.TOPF_Score(bigagg_idx); IDENTITY_NP_HDR{4}='TOPF'; + IDENTITY.NP(si,5)=NP.Coding(bigagg_idx); IDENTITY_NP_HDR{5}='Coding'; + IDENTITY.NP(si,6)=NP.SPAN.Tot(bigagg_idx); IDENTITY_NP_HDR{6}='Span'; + IDENTITY.NP(si,7)=mean([NP.HVLT.T1(bigagg_idx),NP.HVLT.T2(bigagg_idx),NP.HVLT.T3(bigagg_idx)]'); IDENTITY_NP_HDR{7}='HVLT13'; + IDENTITY.NP(si,8)=NP.HVLT.DelayRecall(bigagg_idx); IDENTITY_NP_HDR{8}='HVLTdelay'; + end + IDENTITY.QUEX(si,4)=QUEX.BDI(bigagg_idx,session); IDENTITY_QUEX_HDR{4}='BDI'; + IDENTITY.QUEX(si,5)=QUEX.NSI.tot(bigagg_idx,session); IDENTITY_QUEX_HDR{5}='NSItot'; + IDENTITY.QUEX(si,6)=QUEX.NSI.somatic(bigagg_idx,session); IDENTITY_QUEX_HDR{6}='NSIsom'; + IDENTITY.QUEX(si,7)=QUEX.NSI.cog(bigagg_idx,session); IDENTITY_QUEX_HDR{7}='NSIcog'; + IDENTITY.QUEX(si,8)=QUEX.NSI.emo(bigagg_idx,session); IDENTITY_QUEX_HDR{8}='NSIemo'; + IDENTITY.QUEX(si,9)=QUEX.FRSBE.Tot_B4(bigagg_idx,session); IDENTITY_QUEX_HDR{9}='F_Tot_B4'; + IDENTITY.QUEX(si,10)=QUEX.FRSBE.Tot_Now(bigagg_idx,session); IDENTITY_QUEX_HDR{10}='F_Tot'; + IDENTITY.QUEX(si,11)=EX.EX(bigagg_idx,session); IDENTITY_QUEX_HDR{11}='EX_EX'; + IDENTITY.QUEX(si,12)=EX.CC(bigagg_idx,session); IDENTITY_QUEX_HDR{12}='EX_CC'; + IDENTITY.QUEX(si,13)=EX.FL(bigagg_idx,session); IDENTITY_QUEX_HDR{13}='EX_FL'; + IDENTITY.QUEX(si,14)=EX.WM(bigagg_idx,session); IDENTITY_QUEX_HDR{14}='EX_WM'; + end + elseif group==3 + if any(Q_DEMO.URSI==subno) + bigagg_idx=find(Q_DEMO.URSI==subno); + %^^^^^^^^^^^ + IDENTITY.DEMO(si,4)=Q_DEMO.URSI(bigagg_idx,1); + IDENTITY.DEMO(si,5)=Q_DEMO.Sex_F1(bigagg_idx); + IDENTITY.DEMO(si,6)=Q_DEMO.Age(bigagg_idx); + IDENTITY.DEMO(si,7)=Q_DEMO.SES(bigagg_idx); + % --------------------- + IDENTITY.TBI(si,6)=Q_TBIfields.LOCdurMINS(bigagg_idx); + IDENTITY.TBI(si,9)=Q_TBIfields.YearsSinceInjury(bigagg_idx); IDENTITY_TBI_HDR{9}='Years'; + % --------------------- + IDENTITY.NP(si,4)=Q_NP.TOPF_Score(bigagg_idx); + IDENTITY.NP(si,5)=Q_NP.Coding(bigagg_idx); + IDENTITY.NP(si,6)=Q_NP.SPAN.Tot(bigagg_idx); + IDENTITY.NP(si,7)=mean([Q_NP.HVLT.T1(bigagg_idx),Q_NP.HVLT.T2(bigagg_idx),Q_NP.HVLT.T3(bigagg_idx)]'); + IDENTITY.NP(si,8)=Q_NP.HVLT.DelayRecall(bigagg_idx); + % --------------------- + IDENTITY.QUEX(si,4)=Q_QUEX.BDI(bigagg_idx); + IDENTITY.QUEX(si,5)=Q_QUEX.NSI.tot(bigagg_idx); + IDENTITY.QUEX(si,6)=Q_QUEX.NSI.somatic(bigagg_idx); + IDENTITY.QUEX(si,7)=Q_QUEX.NSI.cog(bigagg_idx); + IDENTITY.QUEX(si,8)=Q_QUEX.NSI.emo(bigagg_idx); + IDENTITY.QUEX(si,9)=Q_QUEX.FRSBE.RAW_Tot_Now(bigagg_idx); + IDENTITY.QUEX(si,10)=Q_QUEX.FRSBE.Tot_Now(bigagg_idx); + end + end + clear bigagg_idx + + % EEG + load([filz(si).name(1:end-5),'V.mat'],'ERP','TRL_ct'); + MEGA_ERP(si,:,:,:)=ERP; + MEGA_TRL_ct(si,:)=TRL_ct; + + clear ERP ISPC ITPC POWER VECTOR RT SYNCH_TOPO TRL_ct subno session group BEH ACC RT POWER_TOPO CORREL_TOPO; + +end + +clear DEMO QUEX NP EX TBIfields Q_* + +cd(homedir); diff --git a/scripts/s2_Kill_Data.m b/scripts/s2_Kill_Data.m new file mode 100644 index 0000000000000000000000000000000000000000..7eb4f1072f534bee0e02539551b003bb90f726e1 --- /dev/null +++ b/scripts/s2_Kill_Data.m @@ -0,0 +1,54 @@ +% Kill Quinn S2 +for si=1:length(IDENTITY.DEMO) + if IDENTITY.DEMO(si,3)==3 && IDENTITY.DEMO(si,2)==2 + IDENTITY.DEMO(si,:)=NaN; IDENTITY.TBI(si,:)=NaN; IDENTITY.NP(si,:)=NaN; IDENTITY.QUEX(si,:)=NaN; +% % MEGA_PWR(si,:,:,:,:)=NaN; +% % MEGA_PHS(si,:,:,:,:)=NaN; +% % MEGA_SYNCH(si,:,:,:,:)=NaN; +% % MEGA_SYNCH_TOPO(si,:,:,:)=NaN; + MEGA_ERP(si,:,:,:)=NaN; + end +end + +% Kill malingering Quinn patient # 43047 (is already excluded... but this will make sure!) +if any(IDENTITY.DEMO(:,1)==43047); BOOM; end + +% Kill any Quinn patients who were Cavanagh patients +% F48 3032(Cav URSI: 30454; Quinn URSI: 35957) & F22 3004(Cav URSI: 69117; Quinn URSI: 48880) +badidx=find(IDENTITY.DEMO(:,1)==35957); +IDENTITY.DEMO(badidx,:)=NaN; IDENTITY.TBI(badidx,:)=NaN; IDENTITY.NP(badidx,:)=NaN; IDENTITY.QUEX(badidx,:)=NaN; clear badidx; +% The other was 3004, who is killed below due to no LOC + +% Kill 2 mTBI with out LOC 3004, 3056 == no LOC +badidx=find(IDENTITY.DEMO(:,1)==3004); +IDENTITY.DEMO(badidx,:)=NaN; IDENTITY.TBI(badidx,:)=NaN; IDENTITY.NP(badidx,:)=NaN; IDENTITY.QUEX(badidx,:)=NaN; clear badidx; +badidx=find(IDENTITY.DEMO(:,1)==3056); +IDENTITY.DEMO(badidx,:)=NaN; IDENTITY.TBI(badidx,:)=NaN; IDENTITY.NP(badidx,:)=NaN; IDENTITY.QUEX(badidx,:)=NaN; clear badidx; + +% Kill people with pre-existing head injuries +badidx=find(IDENTITY.DEMO(:,1)==3024); +IDENTITY.DEMO(badidx,:)=NaN; IDENTITY.TBI(badidx,:)=NaN; IDENTITY.NP(badidx,:)=NaN; IDENTITY.QUEX(badidx,:)=NaN; clear badidx; + +% Kill *sessions* if they had an intervening head injury +for si=1:length(IDENTITY.DEMO) + if IDENTITY.DEMO(si,1)==3034 && IDENTITY.DEMO(si,2)==3 + badidx=si; + end +end +IDENTITY.DEMO(badidx,:)=NaN; IDENTITY.TBI(badidx,:)=NaN; IDENTITY.NP(badidx,:)=NaN; IDENTITY.QUEX(badidx,:)=NaN; clear badidx; + +% Kill people with TOMM score<45 +badidx=find(IDENTITY.DEMO(:,1)==14000); +IDENTITY.DEMO(badidx,:)=NaN; IDENTITY.TBI(badidx,:)=NaN; IDENTITY.NP(badidx,:)=NaN; IDENTITY.QUEX(badidx,:)=NaN; clear badidx; + +% Assessment > 2 weeks +badidx=find(IDENTITY.TBI(:,8)>14); +IDENTITY.DEMO(badidx,:)=NaN; IDENTITY.TBI(badidx,:)=NaN; IDENTITY.NP(badidx,:)=NaN; IDENTITY.QUEX(badidx,:)=NaN; clear badidx; + + + + + + + + diff --git a/scripts/s3_Demographics.m b/scripts/s3_Demographics.m new file mode 100644 index 0000000000000000000000000000000000000000..1fc9be7648eb715da30af98dad05aab21a6a158c --- /dev/null +++ b/scripts/s3_Demographics.m @@ -0,0 +1,88 @@ +% ####################################################################################################### + +% Count +for groupi=1:2 + for time=1:3 + Sx=logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==groupi) ); + TABLE1_COUNT(groupi,time,:)=[sum(Sx),nansum(IDENTITY.DEMO(Sx,5))]; + end +end + +% Other neat stuff +for groupi=1:3 + time=1; + Sx=logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==groupi) ); + TABLE1_VARS(1,groupi,:)=[mean(IDENTITY.DEMO(Sx,6)),std(IDENTITY.DEMO(Sx,6))]; % Age + TABLE1_VARS(2,groupi,:)=[mean(IDENTITY.DEMO(Sx,7)),std(IDENTITY.DEMO(Sx,7))]; % YrsEd + for npi=1:5 + TABLE1_VARS(2+npi,groupi,:)=[nanmean(IDENTITY.NP(Sx,3+npi)),nanstd(IDENTITY.NP(Sx,3+npi))]; + end +end +CTL_Sx=logical( double(IDENTITY.DEMO(:,2)==1) .* double(IDENTITY.DEMO(:,3)==1) ); +Acute_Sx=logical( double(IDENTITY.DEMO(:,2)==1) .* double(IDENTITY.DEMO(:,3)==2) ); +Chronic_Sx=logical( double(IDENTITY.DEMO(:,2)==1) .* double(IDENTITY.DEMO(:,3)==3) ); + +[~,T1_P,~,T1_STATS]=ttest2([IDENTITY.DEMO(CTL_Sx,[6,7]),IDENTITY.NP(CTL_Sx,4:8)],[IDENTITY.DEMO(Acute_Sx,[6,7]),IDENTITY.NP(Acute_Sx,4:8)]) +[~,T1_P,~,T1_STATS]=ttest2([IDENTITY.DEMO(CTL_Sx,[6,7]),IDENTITY.NP(CTL_Sx,4:8)],[IDENTITY.DEMO(Chronic_Sx,[6,7]),IDENTITY.NP(Chronic_Sx,4:8)]) + +% TABLE 2 +IDENTITY.TBI(Acute_Sx,4) % GCS +nanmedian(IDENTITY.TBI(Acute_Sx,6)) % LOCmins median +iqr(IDENTITY.TBI(Acute_Sx,6)) % LOCmins iqr +nansum(IDENTITY.TBI(Acute_Sx,7)) +nanmedian(IDENTITY.TBI(Acute_Sx,8)) % Days median +iqr(IDENTITY.TBI(Acute_Sx,8)) % Days iqr + +nanmedian(IDENTITY.TBI(Chronic_Sx,6)) % LOCmins median +iqr(IDENTITY.TBI(Chronic_Sx,6)) % LOCmins iqr +nansum(IDENTITY.TBI(Chronic_Sx,7)) % Data not here +nanmedian(IDENTITY.TBI(Chronic_Sx,9)) % Days median +iqr(IDENTITY.TBI(Chronic_Sx,9)) % Days iqr + +%% ########################################################################### +clear INTERCOR_Rho INTERCOR_P; +INDEX=[find(strcmp('BDI',IDENTITY_QUEX_HDR)),find(strcmp('NSItot',IDENTITY_QUEX_HDR)),find(strcmp('F_Tot_B4',IDENTITY_QUEX_HDR)),find(strcmp('F_Tot',IDENTITY_QUEX_HDR))]; +COL={'bd','rd','md'}; +SHIFT=[-.05,.05,0]; +figure; hold on; +for groupi=1:2 + for timei=1:3 + Sx=logical( double(IDENTITY.DEMO(:,2)==timei) .* double(IDENTITY.DEMO(:,3)==groupi) ); + for idxi=1:4 + subplot(2,2,idxi); hold on; + ThisN=sum(~isnan(IDENTITY.QUEX(Sx,INDEX(idxi)))); + plot(timei+SHIFT(groupi),nanmean(IDENTITY.QUEX(Sx,INDEX(idxi))),COL{groupi}); + errorbar(timei+SHIFT(groupi),nanmean(IDENTITY.QUEX(Sx,INDEX(idxi))),nanstd(IDENTITY.QUEX(Sx,INDEX(idxi)))./sqrt(ThisN),'k.'); + set(gca,'xlim',[0 4],'xtick',[1:1:3]); + title(IDENTITY_QUEX_HDR{INDEX(idxi)}); + % --- + Sx1=logical( double(IDENTITY.DEMO(:,2)==1) .* double(IDENTITY.DEMO(:,3)==groupi) ); + Sx2=logical( double(IDENTITY.DEMO(:,2)==2) .* double(IDENTITY.DEMO(:,3)==groupi) ); + Sx3=logical( double(IDENTITY.DEMO(:,2)==3) .* double(IDENTITY.DEMO(:,3)==groupi) ); + plot([1+SHIFT(groupi) 2+SHIFT(groupi)],[nanmean(IDENTITY.QUEX(Sx1,INDEX(idxi))), nanmean(IDENTITY.QUEX(Sx2,INDEX(idxi)))],'k-'); + plot([2+SHIFT(groupi) 3+SHIFT(groupi)],[nanmean(IDENTITY.QUEX(Sx2,INDEX(idxi))), nanmean(IDENTITY.QUEX(Sx3,INDEX(idxi)))],'k-'); + % --- + end + if time==1 + [INTERCOR_Rho{groupi},INTERCOR_P{groupi}]=corr(IDENTITY.QUEX(Sx,[4,5,10]),'type','Spearman','rows','pairwise'); + end + end +end +groupi=3; timei=1; +Sx=logical( double(IDENTITY.DEMO(:,2)==timei) .* double(IDENTITY.DEMO(:,3)==groupi) ); +for idxi=1:4 + subplot(2,2,idxi); hold on; + ThisN=sum(~isnan(IDENTITY.QUEX(Sx,INDEX(idxi)))); + plot(timei+SHIFT(groupi),nanmean(IDENTITY.QUEX(Sx,INDEX(idxi))),COL{groupi}); + errorbar(timei+SHIFT(groupi),nanmean(IDENTITY.QUEX(Sx,INDEX(idxi))),nanstd(IDENTITY.QUEX(Sx,INDEX(idxi)))./sqrt(ThisN),'k.'); + set(gca,'xlim',[0 4],'xtick',[1:1:3]); + title(IDENTITY_QUEX_HDR{INDEX(idxi)}); + + + [INTERCOR_Rho{3},INTERCOR_P{3}]=corr(IDENTITY.QUEX(Sx,[4,5,10]),'type','Spearman','rows','pairwise'); +end + + +%% + + diff --git a/scripts/s4_Example_ERPs.m b/scripts/s4_Example_ERPs.m new file mode 100644 index 0000000000000000000000000000000000000000..f363609c205d0961e39e597f7d5c5fd23b9db9c9 --- /dev/null +++ b/scripts/s4_Example_ERPs.m @@ -0,0 +1,30 @@ +%% Example P3a, P3b + +V = logical( double(IDENTITY.DEMO(:,2)<4) ); +SIZEALL=sum( V ); + +figure; subplot(3,3,[1:6]); hold on +plot(tx2disp,squeeze(nanmean( MEGA_ERP(V,NovSite,:,1) ,1)),'k','Linewidth',2); +plot(tx2disp,squeeze(nanmean( MEGA_ERP(V,TargSite,:,2) ,1)),'g','Linewidth',2); +plot(tx2disp,squeeze(nanmean( MEGA_ERP(V,StdSite,:,3) ,1)),'c','Linewidth',2); +shadedErrorBar(tx2disp,squeeze(nanmean( MEGA_ERP(V,NovSite,:,1) ,1)),... + squeeze(nanstd( MEGA_ERP(V,NovSite,:,1) ,1)) ./ sqrt(SIZEALL),'k'); +shadedErrorBar(tx2disp,squeeze(nanmean( MEGA_ERP(V,TargSite,:,2) ,1)),... + squeeze(nanstd( MEGA_ERP(V,TargSite,:,2) ,1)) ./ sqrt(SIZEALL),'g'); +shadedErrorBar(tx2disp,squeeze(nanmean( MEGA_ERP(V,StdSite,:,3) ,1)),... + squeeze(nanstd( MEGA_ERP(V,StdSite,:,3) ,1)) ./ sqrt(SIZEALL),'c'); +legend({'Std','Targ','Nov'},'Location','NorthWest'); +title(['N DataSets =',num2str(SIZEALL)]) + +plot([0 0],[-6 6],'k:'); plot([-500 1000],[0 0],'k:'); +plot([NovT1 NovT1],[4 5],'m:'); plot([NovT2 NovT2],[4 5],'m:'); +plot([TargT1 TargT1],[5.1 6],'r:'); plot([TargT2 TargT2],[5.1 6],'r:'); + +MAPLIMS=[-5 5]; + +subplot(3,3,7); topoplot( squeeze(nanmean(mean( MEGA_ERP(V,:,ERPWINS_tx2disp(1,1):ERPWINS_tx2disp(1,2),1) ,3) ,1)) ,... + BV_Chanlocs_60,'maplimits',MAPLIMS,'emarker2',{StdSite,'d','k'}); title('Std') +subplot(3,3,8); topoplot( squeeze(nanmean(mean( MEGA_ERP(V,:,ERPWINS_tx2disp(2,1):ERPWINS_tx2disp(2,2),2) ,3) ,1)) ,... + BV_Chanlocs_60,'maplimits',MAPLIMS,'emarker2',{TargSite,'d','k'}); title('Targ') +subplot(3,3,9); topoplot( squeeze(nanmean(mean( MEGA_ERP(V,:,ERPWINS_tx2disp(3,1):ERPWINS_tx2disp(3,2),3) ,3) ,1)) ,... + BV_Chanlocs_60,'maplimits',MAPLIMS,'emarker2',{NovSite,'d','k'}); title('Nov') diff --git a/scripts/s5_ERPs_by_Group.m b/scripts/s5_ERPs_by_Group.m new file mode 100644 index 0000000000000000000000000000000000000000..167930315eebff73dc7fd81f22c5d6c571aa0333 --- /dev/null +++ b/scripts/s5_ERPs_by_Group.m @@ -0,0 +1,137 @@ +%% + +TITLES={'Std','Targ','Nov'}; +COL={'b','r','m'}; +YLIM=[-6 8]; + + +figure; +for ai=1:3 + subplot(3,1,ai); hold on; + for gi=1:3 + V = logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==gi) ); + plot(tx2disp,squeeze(nanmean( MEGA_ERP(V,ERPSITE(ai),:,ai) ,1)),COL{gi},'Linewidth',2); + clear V; + end + for gi=1:3 + V = logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==gi) ); + shadedErrorBar(tx2disp,squeeze(nanmean( MEGA_ERP(V,ERPSITE(ai),:,ai) ,1)),squeeze(nanstd( MEGA_ERP(V,ERPSITE(ai),:,ai) ,1))./sqrt(sum(V)),COL{gi}); + clear V; + end + for gi=1:3 + V = logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==gi) ); + plot(tx2disp,squeeze(nanmean( MEGA_ERP(V,ERPSITE(ai),:,ai) ,1)),COL{gi},'Linewidth',2); + set(gca,'ylim',YLIM); + clear V; + end + + plot([ERPWINS(ai,1) ERPWINS(ai,1)],[5 7],'k:'); plot([ERPWINS(ai,2) ERPWINS(ai,2)],[5 7],'k:'); + + title(TITLES{ai}); + + V_ctl = logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==1) ); + V_acute = logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==2) ); + V_chronic = logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==3) ); + ERPs_ctl=squeeze(MEGA_ERP(V_ctl,ERPSITE(ai),:,ai)); + ERPs_acute=squeeze(MEGA_ERP(V_acute,ERPSITE(ai),:,ai)); + ERPs_chronic=squeeze(MEGA_ERP(V_chronic,ERPSITE(ai),:,ai)); + [H,P,CI,STATS]=ttest2(ERPs_ctl,ERPs_acute); P(P>.05)=NaN; P(P<=.05)=1; + plot(tx2disp,-4.*P,'r'); clear H P CI STATS TEMP*; + [H,P,CI,STATS]=ttest2(ERPs_ctl,ERPs_chronic); P(P>.05)=NaN; P(P<=.05)=1; + plot(tx2disp,-4.5.*P,'m'); clear H P CI STATS TEMP*; + + clear V_ctl V_acute V_chronic ERPs_ctl ERPs_acute ERPs_chronic + +end +legend({'CTL','Acute','Chronic'},'Location','NorthWest') + + +%% + +figure; +for ai=1:3 % Condi + subplot(3,1,ai); hold on; + for gi=1:3 % Group + for time=1:3 + V = logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==gi) ); + plot(time,squeeze(nanmean(mean( MEGA_ERP(V,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3),1)),[COL{gi},'d']); + errorbar(time,squeeze(nanmean(mean( MEGA_ERP(V,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3),1)),... + squeeze(nanstd(mean( MEGA_ERP(V,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3),1)) ./sqrt(sum(V)),'k.'); + BIG_OL_N(gi,time)=sum(V); + + clear V; + end + + % --- + V1 = logical( double(IDENTITY.DEMO(:,2)==1) .* double(IDENTITY.DEMO(:,3)==gi) ); + V2 = logical( double(IDENTITY.DEMO(:,2)==2) .* double(IDENTITY.DEMO(:,3)==gi) ); + V3 = logical( double(IDENTITY.DEMO(:,2)==3) .* double(IDENTITY.DEMO(:,3)==gi) ); + plot([1 2],[squeeze(nanmean(mean( MEGA_ERP(V1,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3),1)) ,... + squeeze(nanmean(mean( MEGA_ERP(V2,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3),1))],'k-'); + plot([2 3],[squeeze(nanmean(mean( MEGA_ERP(V2,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3),1)) ,... + squeeze(nanmean(mean( MEGA_ERP(V3,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3),1))],'k-'); + clear V*; + % --- + + end + + set(gca,'xlim',[0 4],'xtick',[1:1:3]) + title(TITLES{ai}); +end + +%% + +STATS{1}=[]; STATS{2}=[]; % Targ, Nov +for si=1:2 % CTL, Acute + Sx=logical( double(IDENTITY.DEMO(:,3)==si) ); + Sx_idxs=unique(IDENTITY.DEMO(Sx,1)); + for sxi=1:length(Sx_idxs) + thisguy=Sx_idxs(sxi); + + FIRST=[]; SECOND=[]; THIRD=[]; + + FIRST=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==1) )); + SECOND=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==2) )); + THIRD=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==3) )); + + ai=2; + ERP1=NaN; ERP2=NaN; ERP3=NaN; + if ~isempty(FIRST), ERP1=mean( MEGA_ERP(FIRST,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3); end + if ~isempty(SECOND), ERP2=mean( MEGA_ERP(SECOND,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3); end + if ~isempty(THIRD), ERP3=mean( MEGA_ERP(THIRD,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3); end + STATS{ai-1}=[STATS{ai-1};thisguy,si,ERP1,ERP2,ERP3]; + + ai=3; + ERP1=NaN; ERP2=NaN; ERP3=NaN; + if ~isempty(FIRST), ERP1=mean( MEGA_ERP(FIRST,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3); end + if ~isempty(SECOND), ERP2=mean( MEGA_ERP(SECOND,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3); end + if ~isempty(THIRD), ERP3=mean( MEGA_ERP(THIRD,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3); end + STATS{ai-1}=[STATS{ai-1};thisguy,si,ERP1,ERP2,ERP3]; + + clear thisguy; + end + clear Sx Sx_idxs; +end + +si=3; clear Sx Sx_idxs; % chronic +Sx=logical( double(IDENTITY.DEMO(:,3)==si) ); +Sx_idxs=unique(IDENTITY.DEMO(Sx,1)); +for sxi=1:length(Sx_idxs) + + thisguy=Sx_idxs(sxi); + FIRST=[]; + FIRST=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==1) )); + + ai=2; + ERP1=NaN; + if ~isempty(FIRST), ERP1=mean( MEGA_ERP(FIRST,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3); end + STATS{ai-1}=[STATS{ai-1};thisguy,si,ERP1,NaN,NaN]; + + ai=3; + ERP1=NaN; + if ~isempty(FIRST), ERP1=mean( MEGA_ERP(FIRST,ERPSITE(ai),ERPWINS_tx2disp(ai,1):ERPWINS_tx2disp(ai,2),ai) ,3); end + STATS{ai-1}=[STATS{ai-1};thisguy,si,ERP1,NaN,NaN]; + + clear thisguy; +end + diff --git a/scripts/s6_Correlations.m b/scripts/s6_Correlations.m new file mode 100644 index 0000000000000000000000000000000000000000..997126183c227e46cbe21a2672a46c59d111a6ff --- /dev/null +++ b/scripts/s6_Correlations.m @@ -0,0 +1,36 @@ +%% + +COLS={'b','r','m'}; +figure; +for si=1:3 + + Sx=logical( double(IDENTITY.DEMO(:,2)==time) .* double(IDENTITY.DEMO(:,3)==si) ); + + % -------------------- + + IV=squeeze(mean(MEGA_ERP(Sx,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)); + + [rho,rho_p]=corr(IV,DV(Sx),'type','Spearman','rows','pairwise'); + [r,p]=corr(IV,DV(Sx),'type','Pearson','rows','pairwise'); + subplot(2,3,si); hold on + scatter(IV,DV(Sx),COLS{si}); lsline + set(gca,'xlim',[-10 20],'ylim',[20 120]); + % text(.1,.7,['df=',num2str(sum(logical(double(~isnan(IV)).*double(~isnan(DV(Sx))))) -2 ),' r=',num2str(r),' p=',num2str(p)],'sc'); + text(.1,.6,['df=',num2str(sum(logical(double(~isnan(IV)).*double(~isnan(DV(Sx))))) -2 ),' rho=',num2str(rho),' p=',num2str(rho_p)],'sc'); + clear rho rho_p r p IV + title( BV_Chanlocs_60(ERPSITE(CONDI4Corr)).labels ); + + % -------------------- + + IV=squeeze(mean(MEGA_ERP(Sx,:,ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)); + + [rho,rho_p]=corr(IV,DV(Sx),'type','Spearman','rows','pairwise'); + subplot(2,3,3+si); hold on + rho_p(rho_p>=.05)=NaN; rho_p(rho_p<.05)=1; rho_p(isnan(rho_p))=0; + topoplot(rho,BV_Chanlocs_60,'emarker2',{find(rho_p==1),'d','k',10,1}); + clear rho rhop_p IV; + + % -------------------- + +end + diff --git a/scripts/s6_Correlations_S1EEG_With_FrSBediffs.m b/scripts/s6_Correlations_S1EEG_With_FrSBediffs.m new file mode 100644 index 0000000000000000000000000000000000000000..85438f2dbe6ee93fda02e1a2dff170554fe5640f --- /dev/null +++ b/scripts/s6_Correlations_S1EEG_With_FrSBediffs.m @@ -0,0 +1,111 @@ +%% +COLS={'c','m'}; +ROWS={'1-2','1-3','2-3'}; +figure; +for si=1:2 + Sx=logical( double(IDENTITY.DEMO(:,3)==si) ); + Sx_idxs=unique(IDENTITY.DEMO(Sx,1)); + + clear IV* DV* *12 *23 *13; + for sxi=1:length(Sx_idxs) + thisguy=Sx_idxs(sxi); + + FIRST=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==1) )); + SECOND=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==2) )); + THIRD=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==3) )); + + if ~isempty(FIRST) && ~isempty(SECOND) + IVs12(sxi,:)= squeeze(mean(MEGA_ERP(FIRST,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)) ; + DVs12(sxi,:)=IDENTITY.QUEX(SECOND,quexidx)-IDENTITY.QUEX(FIRST,quexidx); + age12(sxi,:)=IDENTITY.QUEX(FIRST,6); + TOPF12(sxi,:)=IDENTITY.NP(FIRST,4); + sex12(sxi,:)=IDENTITY.DEMO(FIRST,5); + % NP vars that also predicted dropout + Span12(sxi,:)=IDENTITY.NP(FIRST,6); + Coding12(sxi,:)=IDENTITY.NP(FIRST,5); + else + IVs12(sxi,:)=NaN; + DVs12(sxi,:)=NaN; + age12(sxi,:)=NaN; + TOPF12(sxi,:)=NaN; + sex12(sxi,:)=NaN; + end + + if ~isempty(SECOND) && ~isempty(THIRD) + IVs23(sxi,:)= squeeze(mean(MEGA_ERP(SECOND,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)) ; + DVs23(sxi,:)=IDENTITY.QUEX(THIRD,quexidx)-IDENTITY.QUEX(SECOND,quexidx); + else + IVs23(sxi,:)=NaN; + DVs23(sxi,:)=NaN; + end + + if ~isempty(FIRST) && ~isempty(THIRD) + IVs13(sxi,:)= squeeze(mean(MEGA_ERP(FIRST,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)) ; + DVs13(sxi,:)=IDENTITY.QUEX(THIRD,quexidx)-IDENTITY.QUEX(FIRST,quexidx); + else + IVs13(sxi,:)=NaN; + DVs13(sxi,:)=NaN; + end + + end + + % -------------------- + + [rho,rho_p]=corr(IVs12,DVs12,'type','Spearman','rows','pairwise'); + [r,p]=corr(IVs12,DVs12,'type','Pearson','rows','pairwise'); + subplot(3,2,si); hold on + scatter(IVs12,DVs12,COLS{si}); lsline + set(gca,'xlim',[-10 20],'ylim',[-40 40]); + text(.1,.7,['df=',num2str(sum(logical(double(~isnan(IVs12)).*double(~isnan(DVs12)))) -2 ),' r=',num2str(r),' p=',num2str(p)],'sc'); + text(.1,.6,['df=',num2str(sum(logical(double(~isnan(IVs12)).*double(~isnan(DVs12)))) -2 ),' rho=',num2str(rho),' p=',num2str(rho_p)],'sc'); + clear rho rho_p r p IV + title( [BV_Chanlocs_60(ERPSITE(CONDI4Corr)).labels,' S1EEG ',ROWS{1}] ); + + % -------------------- + + [rho,rho_p]=corr(IVs13,DVs13,'type','Spearman','rows','pairwise'); + [r,p]=corr(IVs13,DVs13,'type','Pearson','rows','pairwise'); + subplot(3,2,si+2); hold on + scatter(IVs13,DVs13,COLS{si}); lsline + set(gca,'xlim',[-10 20],'ylim',[-40 40]); + text(.1,.7,['df=',num2str(sum(logical(double(~isnan(IVs13)).*double(~isnan(DVs13)))) -2 ),' r=',num2str(r),' p=',num2str(p)],'sc'); + text(.1,.6,['df=',num2str(sum(logical(double(~isnan(IVs13)).*double(~isnan(DVs13)))) -2 ),' rho=',num2str(rho),' p=',num2str(rho_p)],'sc'); + clear rho rho_p r p IV + title( [BV_Chanlocs_60(ERPSITE(CONDI4Corr)).labels,' S1EEG ',ROWS{2}] ); + + % -------------------- + + [rho,rho_p]=corr(IVs23,DVs23,'type','Spearman','rows','pairwise'); + [r,p]=corr(IVs23,DVs23,'type','Pearson','rows','pairwise'); + subplot(3,2,si+4); hold on + scatter(IVs23,DVs23,COLS{si}); lsline + set(gca,'xlim',[-10 20],'ylim',[-40 40]); + text(.1,.7,['df=',num2str(sum(logical(double(~isnan(IVs23)).*double(~isnan(DVs23)))) -2 ),' r=',num2str(r),' p=',num2str(p)],'sc'); + text(.1,.6,['df=',num2str(sum(logical(double(~isnan(IVs23)).*double(~isnan(DVs23)))) -2 ),' rho=',num2str(rho),' p=',num2str(rho_p)],'sc'); + clear rho rho_p r p IV + title( [BV_Chanlocs_60(ERPSITE(CONDI4Corr)).labels,' S1EEG ',ROWS{3}] ); + + % -------------------- + +end + + +% Check demographic (S1) vars in the sub-acute group on FrSBe change + +[r,p]=corr(DVs12,age12,'type','Spearman','rows','pairwise') + +[r,p]=corr(DVs12,TOPF12,'type','Spearman','rows','pairwise') + +[H,P,CI,STATS]=ttest2(DVs12(sex12==1),DVs12(sex12==0)) + +[r,p]=corr(DVs12,Span12,'type','Spearman','rows','pairwise') +[r,p]=corr(DVs12,Coding12,'type','Spearman','rows','pairwise') + + + + +[r,p]=corr(DVs13,age12,'type','Spearman','rows','pairwise') +[r,p]=corr(DVs13,TOPF12,'type','Spearman','rows','pairwise') +[H,P,CI,STATS]=ttest2(DVs13(sex12==1),DVs13(sex12==0)) +[r,p]=corr(DVs13,Span12,'type','Spearman','rows','pairwise') +[r,p]=corr(DVs13,Coding12,'type','Spearman','rows','pairwise') diff --git a/scripts/s6_FOR_SPSS.m b/scripts/s6_FOR_SPSS.m new file mode 100644 index 0000000000000000000000000000000000000000..3dd3be7b45471b7e79647be812547de491f1d636 --- /dev/null +++ b/scripts/s6_FOR_SPSS.m @@ -0,0 +1,111 @@ +for CONDI4Corr=2:3; % Std, Targ, Nov + IDENTITY.ERP(:,CONDI4Corr-1)=squeeze(mean(MEGA_ERP(:,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)); +end + +UNIQUE_SX=unique(IDENTITY.DEMO(~isnan(IDENTITY.DEMO(:,1)),1)); +for sxi=1:length(UNIQUE_SX) + thisguy=UNIQUE_SX(sxi); + + FIRST=[]; SECOND=[]; THIRD=[]; + + FIRST=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==1) )); + SECOND=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==2) )); + THIRD=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==3) )); + + + % for standard models + if ~isempty(FIRST) % B/C of bad EEG + FORSPSS(sxi,1)=IDENTITY.DEMO(FIRST,1); FORSPSS_HDR{1}='subno'; + FORSPSS(sxi,2)=IDENTITY.DEMO(FIRST,find(strcmp('session',IDENTITY_DEMO_HDR))); FORSPSS_HDR{2}='session'; + FORSPSS(sxi,3)=IDENTITY.DEMO(FIRST,find(strcmp('group',IDENTITY_DEMO_HDR))); FORSPSS_HDR{3}='group'; + FORSPSS(sxi,4)=IDENTITY.DEMO(FIRST,find(strcmp('SexF1',IDENTITY_DEMO_HDR))); FORSPSS_HDR{4}='SexF1'; + FORSPSS(sxi,5)=IDENTITY.DEMO(FIRST,find(strcmp('Age',IDENTITY_DEMO_HDR))); FORSPSS_HDR{5}='Age'; + FORSPSS(sxi,6)=IDENTITY.NP(FIRST,find(strcmp('TOPF',IDENTITY_NP_HDR))); FORSPSS_HDR{6}='TOPF'; + FORSPSS(sxi,7)=IDENTITY.TBI(FIRST,find(strcmp('Days',IDENTITY_TBI_HDR))); FORSPSS_HDR{7}='Days'; + + FORSPSS(sxi,8)=IDENTITY.QUEX(FIRST,find(strcmp('BDI',IDENTITY_QUEX_HDR))); FORSPSS_HDR{8}='BDI_1'; + FORSPSS(sxi,9)=IDENTITY.QUEX(FIRST,find(strcmp('NSItot',IDENTITY_QUEX_HDR))); FORSPSS_HDR{9}='NSI_1'; + FORSPSS(sxi,10)=IDENTITY.QUEX(FIRST,find(strcmp('F_Tot_B4',IDENTITY_QUEX_HDR))); FORSPSS_HDR{10}='F_B4_1'; + FORSPSS(sxi,11)=IDENTITY.QUEX(FIRST,find(strcmp('F_Tot',IDENTITY_QUEX_HDR))); FORSPSS_HDR{11}='F_Tot_1'; + + FORSPSS(sxi,12)=IDENTITY.ERP(FIRST,1); FORSPSS_HDR{12}='P3b_1'; + FORSPSS(sxi,13)=IDENTITY.ERP(FIRST,2); FORSPSS_HDR{13}='P3a_1'; + else + FORSPSS(sxi,1)=IDENTITY.DEMO(SECOND,1); + FORSPSS(sxi,2)=IDENTITY.DEMO(SECOND,find(strcmp('session',IDENTITY_DEMO_HDR))); + FORSPSS(sxi,3)=IDENTITY.DEMO(SECOND,find(strcmp('group',IDENTITY_DEMO_HDR))); + FORSPSS(sxi,4)=IDENTITY.DEMO(SECOND,find(strcmp('SexF1',IDENTITY_DEMO_HDR))); + FORSPSS(sxi,5)=IDENTITY.DEMO(SECOND,find(strcmp('Age',IDENTITY_DEMO_HDR))); + FORSPSS(sxi,6)=NaN; + FORSPSS(sxi,7)=NaN; + + FORSPSS(sxi,8)=NaN; + FORSPSS(sxi,9)=NaN; + FORSPSS(sxi,10)=NaN; + FORSPSS(sxi,11)=NaN; + + FORSPSS(sxi,12)=NaN; + FORSPSS(sxi,13)=NaN; + end + + if ~isempty(SECOND) + FORSPSS(sxi,14)=IDENTITY.QUEX(SECOND,find(strcmp('BDI',IDENTITY_QUEX_HDR))); FORSPSS_HDR{14}='BDI_2'; + FORSPSS(sxi,15)=IDENTITY.QUEX(SECOND,find(strcmp('NSItot',IDENTITY_QUEX_HDR))); FORSPSS_HDR{15}='NSI_2'; + FORSPSS(sxi,16)=IDENTITY.QUEX(SECOND,find(strcmp('F_Tot_B4',IDENTITY_QUEX_HDR))); FORSPSS_HDR{16}='F_B4_2'; + FORSPSS(sxi,17)=IDENTITY.QUEX(SECOND,find(strcmp('F_Tot',IDENTITY_QUEX_HDR))); FORSPSS_HDR{17}='F_Tot_2'; + FORSPSS(sxi,18)=IDENTITY.ERP(SECOND,1); FORSPSS_HDR{18}='P3b_2'; + FORSPSS(sxi,19)=IDENTITY.ERP(SECOND,2); FORSPSS_HDR{19}='P3a_2'; + else + FORSPSS(sxi,14)=NaN; + FORSPSS(sxi,15)=NaN; + FORSPSS(sxi,16)=NaN; + FORSPSS(sxi,17)=NaN; + FORSPSS(sxi,18)=NaN; + FORSPSS(sxi,19)=NaN; + end + + if ~isempty(THIRD) + FORSPSS(sxi,20)=IDENTITY.QUEX(THIRD,find(strcmp('BDI',IDENTITY_QUEX_HDR))); FORSPSS_HDR{20}='BDI_3'; + FORSPSS(sxi,21)=IDENTITY.QUEX(THIRD,find(strcmp('NSItot',IDENTITY_QUEX_HDR))); FORSPSS_HDR{21}='NSI_3'; + FORSPSS(sxi,22)=IDENTITY.QUEX(THIRD,find(strcmp('F_Tot_B4',IDENTITY_QUEX_HDR))); FORSPSS_HDR{22}='F_B4_3'; + FORSPSS(sxi,23)=IDENTITY.QUEX(THIRD,find(strcmp('F_Tot',IDENTITY_QUEX_HDR))); FORSPSS_HDR{23}='F_Tot_3'; + FORSPSS(sxi,24)=IDENTITY.ERP(THIRD,1); FORSPSS_HDR{24}='P3b_3'; + FORSPSS(sxi,25)=IDENTITY.ERP(THIRD,2); FORSPSS_HDR{25}='P3a_3'; + else + FORSPSS(sxi,20)=NaN; + FORSPSS(sxi,21)=NaN; + FORSPSS(sxi,22)=NaN; + FORSPSS(sxi,23)=NaN; + FORSPSS(sxi,24)=NaN; + FORSPSS(sxi,25)=NaN; + end + + + +end + + + % For Mixed Linear Modeling + for sxi=1:length(IDENTITY.DEMO) + + FORMLM(sxi,1)=IDENTITY.DEMO(sxi,1); FORMLM_HDR{1}='subno'; + FORMLM(sxi,2)=IDENTITY.DEMO(sxi,find(strcmp('session',IDENTITY_DEMO_HDR))); FORMLM_HDR{2}='session'; + FORMLM(sxi,3)=IDENTITY.DEMO(sxi,find(strcmp('group',IDENTITY_DEMO_HDR))); FORMLM_HDR{3}='group'; + FORMLM(sxi,4)=IDENTITY.DEMO(sxi,find(strcmp('SexF1',IDENTITY_DEMO_HDR))); FORMLM_HDR{4}='SexF1'; + FORMLM(sxi,5)=IDENTITY.DEMO(sxi,find(strcmp('Age',IDENTITY_DEMO_HDR))); FORMLM_HDR{5}='Age'; + FORMLM(sxi,6)=IDENTITY.NP(sxi,find(strcmp('TOPF',IDENTITY_NP_HDR))); FORMLM_HDR{6}='TOPF'; + FORMLM(sxi,7)=IDENTITY.TBI(sxi,find(strcmp('Days',IDENTITY_TBI_HDR))); FORMLM_HDR{7}='Days'; + + FORMLM(sxi,8)=IDENTITY.QUEX(sxi,find(strcmp('BDI',IDENTITY_QUEX_HDR))); FORMLM_HDR{8}='BDI'; + FORMLM(sxi,9)=IDENTITY.QUEX(sxi,find(strcmp('NSItot',IDENTITY_QUEX_HDR))); FORMLM_HDR{9}='NSI'; + FORMLM(sxi,10)=IDENTITY.QUEX(sxi,find(strcmp('F_Tot_B4',IDENTITY_QUEX_HDR))); FORMLM_HDR{10}='F_B4'; + FORMLM(sxi,11)=IDENTITY.QUEX(sxi,find(strcmp('F_Tot',IDENTITY_QUEX_HDR))); FORMLM_HDR{11}='F_Tot'; + + FORMLM(sxi,12)=IDENTITY.ERP(sxi,1); FORMLM_HDR{12}='P3b'; + FORMLM(sxi,13)=IDENTITY.ERP(sxi,2); FORMLM_HDR{13}='P3a'; + + end + +FORMLM_HDR=FORMLM_HDR'; +FORSPSS_HDR=FORSPSS_HDR'; + diff --git a/scripts/s7_Mengs_z.m b/scripts/s7_Mengs_z.m new file mode 100644 index 0000000000000000000000000000000000000000..4eee3aa4a595a315b3f922873f782c0dc85e6649 --- /dev/null +++ b/scripts/s7_Mengs_z.m @@ -0,0 +1,51 @@ +% mengz_JFC(r1, r2, r12, n) compares two correlations r1 and r2: +% r1: correlation between X and Y +% r2: correlation between X and Z +% r12: correlation between Y and Z +% n: number of observations used to compute correlations + +%% --------------- +clear Sx CONDI4Corr rhoXY rhoXZ rhoYZ n menghyp mengp mengzscore + +Sx=logical( double(IDENTITY.DEMO(:,2)==1) .* double(IDENTITY.DEMO(:,3)==3) ); +CONDI4Corr=2; +MING_CHRONIC.P3b=squeeze(mean(MEGA_ERP(Sx,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)); +CONDI4Corr=3; +MING_CHRONIC.P3a=squeeze(mean(MEGA_ERP(Sx,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)); + +rhoXY=-.46; % FrSBe & P3b +rhoXZ=.08; % FrSBe & P3a +rhoYZ=corr(MING_CHRONIC.P3b,MING_CHRONIC.P3a,'type','Spearman','rows','pairwise'); % P3a & PBb +n=sum(Sx); +[menghyp,mengp,mengzscore] = mengz_JFC(rhoXY,rhoXZ,rhoYZ,n) + +%% --------------- +clear Sx CONDI4Corr rhoXY rhoXZ rhoYZ n menghyp mengp mengzscore + +Sx=logical( double(IDENTITY.DEMO(:,2)==1) .* double(IDENTITY.DEMO(:,3)==2) ); +CONDI4Corr=2; +MING_ACUTE.P3b=squeeze(mean(MEGA_ERP(Sx,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)); +CONDI4Corr=3; +MING_ACUTE.P3a=squeeze(mean(MEGA_ERP(Sx,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)); + +rhoXY=-.11; % FrSBe & P3b +rhoXZ=-.44; % FrSBe & P3a +rhoYZ=corr(MING_ACUTE.P3b,MING_ACUTE.P3a,'type','Spearman','rows','pairwise'); % P3a & PBb +n=sum(Sx); +[menghyp,mengp,mengzscore] = mengz_JFC(rhoXY,rhoXZ,rhoYZ,n) + +%% --------------- +clear Sx CONDI4Corr rhoXY rhoXZ rhoYZ n menghyp mengp mengzscore + +Sx=logical( double(IDENTITY.DEMO(:,2)==2) .* double(IDENTITY.DEMO(:,3)==2) ); +CONDI4Corr=2; +MING_ACUTE_S2.P3b=squeeze(mean(MEGA_ERP(Sx,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)); +CONDI4Corr=3; +MING_ACUTE_S2.P3a=squeeze(mean(MEGA_ERP(Sx,ERPSITE(CONDI4Corr),ERPWINS_tx2disp(CONDI4Corr,1):ERPWINS_tx2disp(CONDI4Corr,2),CONDI4Corr),3)); + +rhoXY=-.11; % FrSBe & P3b +rhoXZ=-.49; % FrSBe & P3a +rhoYZ=corr(MING_ACUTE_S2.P3b,MING_ACUTE_S2.P3a,'type','Spearman','rows','pairwise'); % P3a & PBb +n=sum(Sx); +[menghyp,mengp,mengzscore] = mengz_JFC(rhoXY,rhoXZ,rhoYZ,n) + diff --git a/scripts/sx_Predict_Attrition.m b/scripts/sx_Predict_Attrition.m new file mode 100644 index 0000000000000000000000000000000000000000..adc02b996b7128a60f320cddbaa1187c1e84a75c --- /dev/null +++ b/scripts/sx_Predict_Attrition.m @@ -0,0 +1,85 @@ +%% + +acuteTBI=IDENTITY.DEMO(:,3)==2; +Sx_idxs=unique(IDENTITY.DEMO(acuteTBI,1)); + +AttritionPredictors=NaN(length(Sx_idxs),14); +for sxi=1:length(Sx_idxs) + thisguy=Sx_idxs(sxi); + + FIRST=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==1) )); + SECOND=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==2) )); + THIRD=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==3) )); + + if ~isempty(FIRST), Attrition(sxi,1)=1; + AttritionPredictors_HDR={'age';'sex';'TOPF';'Coding';'Span';'HVLT13';'HVLTDelay';'GCS';'LOCtime';'LOM';'Days';'BDI';'NSI';'FrSBe'}; + AttritionPredictors(sxi,:)=[... + IDENTITY.DEMO(FIRST,6:7),... + IDENTITY.NP(FIRST,4:8),... + IDENTITY.TBI(FIRST,[4,6:8]),... + IDENTITY.QUEX(FIRST,[4,5,10]),... + ]; + else Attrition(sxi,1)=0; end + if ~isempty(SECOND), Attrition(sxi,2)=1; else Attrition(sxi,2)=0; end + if ~isempty(THIRD), Attrition(sxi,3)=1; else Attrition(sxi,3)=0; end + + clear thisguy FIRST SECOND THIRD; +end + +% Clear NaNs +AttritionPredictors=AttritionPredictors(~isnan(AttritionPredictors(:,1)),:); +Attrition=Attrition(~isnan(AttritionPredictors(:,1)),:); + + +for atti=1:size(AttritionPredictors,2) + for sessi=1:3 % 1 doesn't really make sense, but added here to keep columns nice + + A=AttritionPredictors(:,atti); + B=Attrition(:,sessi); + + A2=A(~isnan(A)); + B2=B(~isnan(A)); + + % B_acc is [constant, v1, v2,v1*v2] [validated by SPSS] + % STATS_acc.p is the p value for each + [B_acc,DEV_acc,STATS_acc] = glmfit(zscore(A2),B2, 'binomial','link','logit'); + Predictors_Logistic{sessi}(atti,1)=B_acc(2); + Predictors_Logistic{sessi}(atti,2)=STATS_acc.p(2); + + [~,P,~,STATS]=ttest2(A2(B2==1),A2(B2==0)); + Predictors_t{sessi}(atti,1)=STATS.tstat; + Predictors_t{sessi}(atti,2)=P; + + [P,~,U]=ranksum(A2(B2==1),A2(B2==0)); + Predictors_u{sessi}(atti,1)=U.zval; + Predictors_u{sessi}(atti,2)=P; + + clear B_acc DEV_acc STATS_acc A B A2 B2 STATS P U; + end +end + +% % % S2: +% Logistic - 5 +% t-test - 5 +% U-test - 5 + +% % % S3: +% Logistic - 3,5 [almost 4] +% t-test - 3,5 [almost 4] +% U-test - 3,4,5 + +% 3=TOPF +% 4=Coding +% 5=Span + +% Sess 2 dropouts: +nanmean(AttritionPredictors(Attrition(:,2)==0,3:5)) +% Sess 2 stays: +nanmean(AttritionPredictors(Attrition(:,2)==1,3:5)) + +% Sess 3 dropouts: +nanmean(AttritionPredictors(Attrition(:,3)==0,3:5)) +% Sess 3 stays: +nanmean(AttritionPredictors(Attrition(:,3)==1,3:5)) + +% So Lower Span predicts S2 dropout and Lower Span, Coding, and TOPF predict S3 dropout diff --git a/scripts/xticklabel_rotate.m b/scripts/xticklabel_rotate.m new file mode 100644 index 0000000000000000000000000000000000000000..bfcec18037b863fdc37e3582c6dcc3d83563c05f --- /dev/null +++ b/scripts/xticklabel_rotate.m @@ -0,0 +1,269 @@ +function hText = xticklabel_rotate(XTick,rot,varargin) +%hText = xticklabel_rotate(XTick,rot,XTickLabel,varargin) Rotate XTickLabel +% +% Syntax: xticklabel_rotate +% +% Input: +% {opt} XTick - vector array of XTick positions & values (numeric) +% uses current XTick values or XTickLabel cell array by +% default (if empty) +% {opt} rot - angle of rotation in degrees, 90 by default +% {opt} XTickLabel - cell array of label strings +% {opt} [var] - "Property-value" pairs passed to text generator +% ex: 'interpreter','none' +% 'Color','m','Fontweight','bold' +% +% Output: hText - handle vector to text labels +% +% Example 1: Rotate existing XTickLabels at their current position by 90 +% xticklabel_rotate +% +% Example 2: Rotate existing XTickLabels at their current position by 45 and change +% font size +% xticklabel_rotate([],45,[],'Fontsize',14) +% +% Example 3: Set the positions of the XTicks and rotate them 90 +% figure; plot([1960:2004],randn(45,1)); xlim([1960 2004]); +% xticklabel_rotate([1960:2:2004]); +% +% Example 4: Use text labels at XTick positions rotated 45 without tex interpreter +% xticklabel_rotate(XTick,45,NameFields,'interpreter','none'); +% +% Example 5: Use text labels rotated 90 at current positions +% xticklabel_rotate([],90,NameFields); +% +% Example 6: Multiline labels +% figure;plot([1:4],[1:4]) +% axis([0.5 4.5 1 4]) +% xticklabel_rotate([1:4],45,{{'aaa' 'AA'};{'bbb' 'AA'};{'ccc' 'BB'};{'ddd' 'BB'}}) +% +% Note : you can not RE-RUN xticklabel_rotate on the same graph. +% + + + +% This is a modified version of xticklabel_rotate90 by Denis Gilbert +% Modifications include Text labels (in the form of cell array) +% Arbitrary angle rotation +% Output of text handles +% Resizing of axes and title/xlabel/ylabel positions to maintain same overall size +% and keep text on plot +% (handles small window resizing after, but not well due to proportional placement with +% fixed font size. To fix this would require a serious resize function) +% Uses current XTick by default +% Uses current XTickLabel is different from XTick values (meaning has been already defined) + +% Brian FG Katz +% bfgkatz@hotmail.com +% 23-05-03 +% Modified 03-11-06 after user comment +% Allow for exisiting XTickLabel cell array +% Modified 03-03-2006 +% Allow for labels top located (after user comment) +% Allow case for single XTickLabelName (after user comment) +% Reduced the degree of resizing +% Modified 11-jun-2010 +% Response to numerous suggestions on MatlabCentral to improve certain +% errors. +% Modified 23-sep-2014 +% Allow for mutliline labels + + +% Other m-files required: cell2mat +% Subfunctions: none +% MAT-files required: none +% +% See also: xticklabel_rotate90, TEXT, SET + +% Based on xticklabel_rotate90 +% Author: Denis Gilbert, Ph.D., physical oceanography +% Maurice Lamontagne Institute, Dept. of Fisheries and Oceans Canada +% email: gilbertd@dfo-mpo.gc.ca Web: http://www.qc.dfo-mpo.gc.ca/iml/ +% February 1998; Last revision: 24-Mar-2003 + +% check to see if xticklabel_rotate has already been here (no other reason for this to happen) +if isempty(get(gca,'XTickLabel')), + error('xticklabel_rotate : can not process, either xticklabel_rotate has already been run or XTickLabel field has been erased') ; +end + +% if no XTickLabel AND no XTick are defined use the current XTickLabel +%if nargin < 3 & (~exist('XTick') | isempty(XTick)), +% Modified with forum comment by "Nathan Pust" allow the current text labels to be used and property value pairs to be changed for those labels +if (nargin < 3 || isempty(varargin{1})) & (~exist('XTick') | isempty(XTick)), + xTickLabels = get(gca,'XTickLabel') ; % use current XTickLabel + if ~iscell(xTickLabels) + % remove trailing spaces if exist (typical with auto generated XTickLabel) + temp1 = num2cell(xTickLabels,2) ; + for loop = 1:length(temp1), + temp1{loop} = deblank(temp1{loop}) ; + end + xTickLabels = temp1 ; + end +varargin = varargin(2:length(varargin)); +end + +% if no XTick is defined use the current XTick +if (~exist('XTick') | isempty(XTick)), + XTick = get(gca,'XTick') ; % use current XTick +end + +%Make XTick a column vector +XTick = XTick(:); + +if ~exist('xTickLabels'), + % Define the xtickLabels + % If XtickLabel is passed as a cell array then use the text + if (length(varargin)>0) & (iscell(varargin{1})), + xTickLabels = varargin{1}; + varargin = varargin(2:length(varargin)); + else + xTickLabels = num2str(XTick); + end +end + +if length(XTick) ~= length(xTickLabels), + error('xticklabel_rotate : must have same number of elements in "XTick" and "XTickLabel"') ; +end + +%Set the Xtick locations and set XTicklabel to an empty string +set(gca,'XTick',XTick,'XTickLabel','') + +if nargin < 2, + rot = 90 ; +end + +% Determine the location of the labels based on the position +% of the xlabel +hxLabel = get(gca,'XLabel'); % Handle to xlabel +xLabelString = get(hxLabel,'String'); + +% if ~isempty(xLabelString) +% warning('You may need to manually reset the XLABEL vertical position') +% end + +set(hxLabel,'Units','data'); +xLabelPosition = get(hxLabel,'Position'); +y = xLabelPosition(2); + +%CODE below was modified following suggestions from Urs Schwarz +y=repmat(y,size(XTick,1),1); +% retrieve current axis' fontsize +fs = get(gca,'fontsize'); + +if ~iscell(xTickLabels) + % Place the new xTickLabels by creating TEXT objects + hText = text(XTick, y, xTickLabels,'fontsize',fs); +else + % Place multi-line text approximately where tick labels belong + for cnt=1:length(XTick), + hText(cnt) = text(XTick(cnt),y(cnt),xTickLabels{cnt},... + 'VerticalAlignment','top', 'UserData','xtick'); + end +end + +% Rotate the text objects by ROT degrees +%set(hText,'Rotation',rot,'HorizontalAlignment','right',varargin{:}) +% Modified with modified forum comment by "Korey Y" to deal with labels at top +% Further edits added for axis position +xAxisLocation = get(gca, 'XAxisLocation'); +if strcmp(xAxisLocation,'bottom') + set(hText,'Rotation',rot,'HorizontalAlignment','right',varargin{:}) +else + set(hText,'Rotation',rot,'HorizontalAlignment','left',varargin{:}) +end + +% Adjust the size of the axis to accomodate for longest label (like if they are text ones) +% This approach keeps the top of the graph at the same place and tries to keep xlabel at the same place +% This approach keeps the right side of the graph at the same place + +set(get(gca,'xlabel'),'units','data') ; + labxorigpos_data = get(get(gca,'xlabel'),'position') ; +set(get(gca,'ylabel'),'units','data') ; + labyorigpos_data = get(get(gca,'ylabel'),'position') ; +set(get(gca,'title'),'units','data') ; + labtorigpos_data = get(get(gca,'title'),'position') ; + +set(gca,'units','pixel') ; +set(hText,'units','pixel') ; +set(get(gca,'xlabel'),'units','pixel') ; +set(get(gca,'ylabel'),'units','pixel') ; +% set(gca,'units','normalized') ; +% set(hText,'units','normalized') ; +% set(get(gca,'xlabel'),'units','normalized') ; +% set(get(gca,'ylabel'),'units','normalized') ; + +origpos = get(gca,'position') ; + +% textsizes = cell2mat(get(hText,'extent')) ; +% Modified with forum comment from "Peter Pan" to deal with case when only one XTickLabelName is given. +x = get( hText, 'extent' ); +if iscell( x ) == true + textsizes = cell2mat( x ) ; +else + textsizes = x; +end + +largest = max(textsizes(:,3)) ; +longest = max(textsizes(:,4)) ; + +laborigext = get(get(gca,'xlabel'),'extent') ; +laborigpos = get(get(gca,'xlabel'),'position') ; + +labyorigext = get(get(gca,'ylabel'),'extent') ; +labyorigpos = get(get(gca,'ylabel'),'position') ; +leftlabdist = labyorigpos(1) + labyorigext(1) ; + +% assume first entry is the farthest left +leftpos = get(hText(1),'position') ; +leftext = get(hText(1),'extent') ; +leftdist = leftpos(1) + leftext(1) ; +if leftdist > 0, leftdist = 0 ; end % only correct for off screen problems + +% botdist = origpos(2) + laborigpos(2) ; +% newpos = [origpos(1)-leftdist longest+botdist origpos(3)+leftdist origpos(4)-longest+origpos(2)-botdist] +% +% Modified to allow for top axis labels and to minimize axis resizing +if strcmp(xAxisLocation,'bottom') + newpos = [origpos(1)-(min(leftdist,labyorigpos(1)))+labyorigpos(1) ... + origpos(2)+((longest+laborigpos(2))-get(gca,'FontSize')) ... + origpos(3)-(min(leftdist,labyorigpos(1)))+labyorigpos(1)-largest ... + origpos(4)-((longest+laborigpos(2))-get(gca,'FontSize'))] ; +else + newpos = [origpos(1)-(min(leftdist,labyorigpos(1)))+labyorigpos(1) ... + origpos(2) ... + origpos(3)-(min(leftdist,labyorigpos(1)))+labyorigpos(1)-largest ... + origpos(4)-(longest)+get(gca,'FontSize')] ; +end +set(gca,'position',newpos) ; + +% readjust position of text labels after resize of plot +set(hText,'units','data') ; +for loop= 1:length(hText), + set(hText(loop),'position',[XTick(loop), y(loop)]) ; +end + +% adjust position of xlabel and ylabel +laborigpos = get(get(gca,'xlabel'),'position') ; +set(get(gca,'xlabel'),'position',[laborigpos(1) laborigpos(2)-longest 0]) ; + +% switch to data coord and fix it all +set(get(gca,'ylabel'),'units','data') ; +set(get(gca,'ylabel'),'position',labyorigpos_data) ; +set(get(gca,'title'),'position',labtorigpos_data) ; + +set(get(gca,'xlabel'),'units','data') ; + labxorigpos_data_new = get(get(gca,'xlabel'),'position') ; +set(get(gca,'xlabel'),'position',[labxorigpos_data(1) labxorigpos_data_new(2)]) ; + + +% Reset all units to normalized to allow future resizing +set(get(gca,'xlabel'),'units','normalized') ; +set(get(gca,'ylabel'),'units','normalized') ; +set(get(gca,'title'),'units','normalized') ; +set(hText,'units','normalized') ; +set(gca,'units','normalized') ; + +if nargout < 1, + clear hText +end +